polygraf-ai/ACL-abstracts · Datasets at Hugging Face

inproceedings

kolarova-vernerova-2022-nomvallex

{N}om{V}allex: A Valency Lexicon of {C}zech Nouns and Adjectives

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.143/

Kol{\'a}{\v{r}}ov{\'a}, Veronika and Vernerov{\'a}, Anna

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1344--1352

We present NomVallex, a manually annotated valency lexicon of Czech nouns and adjectives. The lexicon is created in the theoretical framework of the Functional Generative Description and based on corpus data. In total, NomVallex 2.0 is comprised of 1027 lexical units contained in 570 lexemes, covering the following part-of-speech and derivational categories: deverbal and deadjectival nouns, and deverbal, denominal, deadjectival and primary adjectives. Valency properties of a lexical unit are captured in a valency frame which is modeled as a sequence of valency slots, supplemented with a list of morphemic forms. In order to make it possible to study the relationship between valency behavior of base words and their derivatives, lexical units of nouns and adjectives in NomVallex are linked to their respective base words, contained either in NomVallex itself or, in case of verbs, in a valency lexicon of Czech verbs called VALLEX. NomVallex enables a comparison of valency properties of a significant number of Czech nominals with their base words, both manually and in an automatic way; as such, we can address the theoretical question of argument inheritance, concentrating on systemic and non-systemic valency behavior.

null

24,550

inproceedings

aldezabal-etal-2022-tzos

{TZOS}: an Online Terminology Database Aimed at Working on {B}asque Academic Terminology Collaboratively

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.144/

Aldezabal, Izaskun and Arriola, Jose Mari and Otegi, Arantxa

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1353--1359

Terminology databases are highly useful for the dissemination of specialized knowledge. In this paper we present TZOS, an online terminology database to work on Basque academic terminology collaboratively. We show how this resource integrates the Communicative Theory of Terminology, together with the methodological matters, how it is connected with real corpus GARATERM, which terminology issues arise when terms are collected and future perspectives. The main objectives of this work are to develop basic tools to research academic registers and make the terminology collected by expert users available to the community. Even though TZOS has been designed for an educational context, its flexible structure makes possible to extend it also to the professional area. In this way, we have built IZIBI-TZOS which is a Civil Engineering oriented version of TZOS. These resources are already publicly available, and the ongoing work is towards the interlinking with other lexical resources by applying linking data principles.

null

24,551

inproceedings

klenner-gohring-2022-animacy

{A}nimacy Denoting {G}erman Nouns: Annotation and Classification

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.145/

Klenner, Manfred and G{\"ohring, Anne

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1360--1364

In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.

null

24,552

inproceedings

troiano-etal-2022-x

x-en{VENT}: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.146/

Troiano, Enrica and Oberlaender, Laura Ana Maria and Wegge, Maximilian and Klinger, Roman

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1365--1375

Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. So far, this task has been the recognition of the emotion of writers and readers, as well as that of entities mentioned in the text. We argue that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode. Based on appraisal theories in psychology, which treat emotions as reactions to events, we compile an English corpus of written event descriptions. The descriptions depict emotion-eliciting circumstances, and they contain mentions of people who responded emotionally. We annotate all experiencers, including the original author, with the emotions they likely felt. In addition, we link them to the event they found salient (which can be different for different experiencers in a text) by annotating event properties, or appraisals (e.g., the perceived event undesirability, the uncertainty of its outcome). Our analysis reveals patterns in the co-occurrence of people`s emotions in interaction. Hence, this richly-annotated resource provides useful data to study emotions and event evaluations from the perspective of different roles, and it enables the development of experiencer-specific emotion and appraisal classification systems.

null

24,553

inproceedings

gohring-klenner-2022-polar

Polar Quantification of Actor Noun Phrases for {G}erman

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.147/

G{\"ohring, Anne and Klenner, Manfred

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1376--1380

In this paper, we discuss work that strives to measure the degree of negativity - the negative polar load - of noun phrases, especially those denoting actors. Since no gold standard data is available for German for this quantification task, we generated a silver standard and used it to fine-tune a BERT-based intensity regressor. We evaluated the quality of the silver standard empirically and found that our lexicon-based quantification metric showed a strong correlation with human annotators.

null

24,554

inproceedings

priban-steinberger-2022-czech

{C}zech Dataset for Cross-lingual Subjectivity Classification

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.148/

P{\v{r}}ib{\'a}{\v{n}}, Pavel and Steinberger, Josef

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1381--1391

In this paper, we introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multilingual models to transfer knowledge between Czech and English and vice versa. Two annotators annotated the dataset reaching 0.83 of the Cohen`s K inter-annotator agreement. To the best of our knowledge, this is the first subjectivity dataset for the Czech language. We also created an additional dataset that consists of 200k automatically labeled sentences. Both datasets are freely available for research purposes. Furthermore, we fine-tune five pre-trained BERT-like models to set a monolingual baseline for the new dataset and we achieve 93.56{\%} of accuracy. We fine-tune models on the existing English dataset for which we obtained results that are on par with the current state-of-the-art results. Finally, we perform zero-shot cross-lingual subjectivity classification between Czech and English to verify the usability of our dataset as the cross-lingual benchmark. We compare and discuss the cross-lingual and monolingual results and the ability of multilingual models to transfer knowledge between languages.

null

24,555

inproceedings

ciobotaru-etal-2022-red

{RED} v2: Enhancing {RED} Dataset for Multi-Label Emotion Detection

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.149/

Ciobotaru, Alexandra and Constantinescu, Mihai Vlad and Dinu, Liviu P. and Dumitrescu, Stefan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1392--1399

RED (Romanian Emotion Dataset) is a machine learning-based resource developed for the automatic detection of emotions in Romanian texts, containing single-label annotated tweets with one of the following emotions: joy, fear, sadness, anger and neutral. In this work, we propose REDv2, an open-source extension of RED by adding two more emotions, trust and surprise, and by widening the annotation schema so that the resulted novel dataset is multi-label. We show the overall reliability of our dataset by computing inter-annotator agreements per tweet using a formula suitable for our annotation setup and we aggregate all annotators' opinions into two variants of ground truth, one suitable for multi-label classification and the other suitable for text regression. We propose strong baselines with two transformer models, the Romanian BERT and the multilingual XLM-Roberta model, in both categorical and regression settings.

null

24,556

inproceedings

ortmann-2022-fine

Fine-Grained Error Analysis and Fair Evaluation of Labeled Spans

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.150/

Ortmann, Katrin

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1400--1407

The traditional evaluation of labeled spans with precision, recall, and F1-score has undesirable effects due to double penalties. Annotations with incorrect label or boundaries count as two errors instead of one, despite being closer to the target annotation than false positives or false negatives. In this paper, new error types are introduced, which more accurately reflect true annotation quality and ensure that every annotation counts only once. An algorithm for error identification in flat and multi-level annotations is presented and complemented with a proposal on how to calculate meaningful precision, recall, and F1-scores based on the more fine-grained error types. The exemplary application to three different annotation tasks (NER, chunking, parsing) shows that the suggested procedure not only prevents double penalties but also allows for a more detailed error analysis, thereby providing more insight into the actual weaknesses of a system.

null

24,557

inproceedings

epure-hennequin-2022-probing

Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.151/

Epure, Elena V. and Hennequin, Romain

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1408--1417

Multiple works have proposed to probe language models (LMs) for generalization in named entity (NE) typing (NET) and recognition (NER). However, little has been done in this direction for auto-regressive models despite their popularity and potential to express a wide variety of NLP tasks in the same unified format. We propose a new methodology to probe auto-regressive LMs for NET and NER generalization, which draws inspiration from human linguistic behavior, by resorting to meta-learning. We study NEs of various types individually by designing a zero-shot transfer strategy for NET. Then, we probe the model for NER by providing a few examples at inference. We introduce a novel procedure to assess the model`s memorization of NEs and report the memorization`s impact on the results. Our findings show that: 1) GPT2, a common pre-trained auto-regressive LM, without any fine-tuning for NET or NER, performs the tasksfairly well; 2) name irregularity when common for a NE type could be an effective exploitable cue; 3) the model seems to rely more on NE than contextual cues in few-shot NER; 4) NEs with words absent during LM pre-training are very challenging for both NET and NER.

null

24,558

inproceedings

van-der-goot-etal-2022-frustratingly

Frustratingly Easy Performance Improvements for Low-resource Setups: A Tale on {BERT} and Segment Embeddings

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.152/

van der Goot, Rob and M{\"uller-Eberstein, Max and Plank, Barbara

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1418--1427

As input representation for each sub-word, the original BERT architecture proposes the sum of the sub-word embedding, position embedding and a segment embedding. Sub-word and position embeddings are well-known and studied, and encode lexical information and word position, respectively. In contrast, segment embeddings are less known and have so far received no attention, despite being ubiquitous in large pre-trained language models. The key idea of segment embeddings is to encode to which of the two sentences (segments) a word belongs to {---} the intuition is to inform the model about the separation of sentences for the next sentence prediction pre-training task. However, little is known on whether the choice of segment impacts performance. In this work, we try to fill this gap and empirically study the impact of the segment embedding during inference time for a variety of pre-trained embeddings and target tasks. We hypothesize that for single-sentence prediction tasks performance is not affected {---} neither in mono- nor multilingual setups {---} while it matters when swapping segment IDs in paired-sentence tasks. To our surprise, this is not the case. Although for classification tasks and monolingual BERT models no large differences are observed, particularly word-level multilingual prediction tasks are heavily impacted. For low-resource syntactic tasks, we observe impacts of segment embedding and multilingual BERT choice. We find that the default setting for the most used multilingual BERT model underperforms heavily, and a simple swap of the segment embeddings yields an average improvement of 2.5 points absolute LAS score for dependency parsing over 9 different treebanks.

null

24,559

inproceedings

navarretta-haltrup-hansen-2022-subject

The Subject Annotations of the {D}anish Parliament Corpus (2009-2017) - Evaluated with Automatic Multi-label Classification

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.153/

Navarretta, Costanza and Haltrup Hansen, Dorte

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1428--1436

This paper addresses the semi-automatic annotation of subjects, also called policy areas, in the Danish Parliament Corpus (2009-2017) v.2. Recently, the corpus has been made available through the CLARIN-DK repository, the Danish node of the European CLARIN infrastructure. The paper also contains an analysis of the subjects in the corpus, and a description of multi-label classification experiments act to verify the consistency of the subject annotation and the utility of the corpus for training classifiers on this type of data. The analysis of the corpus comprises an investigation of how often the parliament members addressed each subject and the relation between subjects and gender of the speaker. The classification experiments show that classifiers can determine the two co-occurring subjects of the speeches from the agenda titles with a performance similar to that of human annotators. Moreover, a multilayer perceptron achieved an F1-score of 0.68 on the same task when trained on bag of words vectors obtained from the speeches' lemmas. This is an improvement of more than 0.6 with respect to the baseline, a majority classifier that accounts for the frequency of the classes. The result is promising given the high number of subject combinations (186) and the skewness of the data.

null

24,560

inproceedings

richardson-wiles-2022-systematic

A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.154/

Richardson, Ashleigh and Wiles, Janet

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1437--1443

A significant challenge in developing translation systems for the world`s {\ensuremath{\sim}}7,000 languages is that very few have sufficient data for state-of-the-art techniques. Transfer learning is a promising direction for low-resource neural machine translation (NMT), but introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. When pre-training an NMT system for low-resource translation, the pre-training task is often chosen based on data abundance and similarity to the main task. Factors such as dataset sizes and similarity have typically been analysed independently in previous studies, due to the computational cost associated with systematic studies. However, these factors are not independent. We conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. We replicated the common finding that more data was beneficial in bilingual systems, but also found a statistically significant interaction between the three factors, which reduced the effectiveness of large pre-training datasets for some main task dataset sizes (p-value {\ensuremath{<}} 0.0018). The surprising trends identified in these interactions indicate that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural methods.

null

24,561

inproceedings

ocal-etal-2022-holistic

Holistic Evaluation of Automatic {T}ime{ML} Annotators

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.155/

Ocal, Mustafa and Perez, Adrian and Radas, Antonela and Finlayson, Mark

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1444--1453

TimeML is a scheme for representing temporal information (times, events, {\&} temporal relations) in texts. Although automatic TimeML annotation is challenging, there has been notable progress, with F1s of 0.8{--}0.9 for events and time detection subtasks, and F1s of 0.5{--}0.7 for relation extraction. Individually, these subtask results are reasonable, even good, but when combined to generate a full TimeML graph, is overall performance still acceptable? We present a novel suite of eight metrics, combined with a new graph-transformation experimental design, for holistic evaluation of TimeML graphs. We apply these metrics to four automatic TimeML annotation systems (CAEVO, TARSQI, CATENA, and ClearTK). We show that on average 1/3 of the TimeML graphs produced using these systems are inconsistent, and there is on average 1/5 more temporal indeterminacy than the gold-standard. We also show that the automatically generated graphs are on average 109 edits from the gold-standard, which is 1/3 toward complete replacement. Finally, we show that the relationship individual subtask performance and graph quality is non-linear: small errors in TimeML subtasks result in rapid degradation of final graph quality. These results suggest current automatic TimeML annotators are far from optimal and significant further improvement would be useful.

null

24,562

inproceedings

gladkoff-etal-2022-measuring

Measuring Uncertainty in Translation Quality Evaluation ({TQE})

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.156/

Gladkoff, Serge and Sorokina, Irina and Han, Lifeng and Alekseeva, Alexandra

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1454--1461

From both human translators (HT) and machine translation (MT) researchers' point of view, translation quality evaluation (TQE) is an essential task. Translation service providers (TSPs) have to deliver large volumes of translations which meet customer specifications with harsh constraints of required quality level in tight time-frames and costs. MT researchers strive to make their models better, which also requires reliable quality evaluation. While automatic machine translation evaluation (MTE) metrics and quality estimation (QE) tools are widely available and easy to access, existing automated tools are not good enough, and human assessment from professional translators (HAP) are often chosen as the golden standard (CITATION). Human evaluations, however, are often accused of having low reliability and agreement. Is this caused by subjectivity or statistics is at play? How to avoid the entire text to be checked and be more efficient with TQE from cost and efficiency perspectives, and what is the optimal sample size of the translated text, so as to reliably estimate the translation quality of the entire material? This work carries out such a motivated research to correctly estimate the confidence intervals (CITATION) depending on the sample size of translated text, e.g. the amount of words or sentences, that needs to be processed on TQE workflow step for confident and reliable evaluation of overall translation quality. The methodology we applied for this work is from Bernoulli Statistical Distribution Modelling (BSDM) and Monte Carlo Sampling Analysis (MCSA).

null

24,563

inproceedings

altammami-atwell-2022-challenging

Challenging the Transformer-based models with a Classical {A}rabic dataset: {Q}uran and {H}adith

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.157/

Altammami, Shatha and Atwell, Eric

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1462--1471

Transformer-based models showed near-perfect results on several downstream tasks. However, their performance on classical Arabic texts is largely unexplored. To fill this gap, we evaluate monolingual, bilingual, and multilingual state-of-the-art models to detect relatedness between the Quran (Muslim holy book) and the Hadith (Prophet Muhammed teachings), which are complex classical Arabic texts with underlying meanings that require deep human understanding. To do this, we carefully built a dataset of Quran-verse and Hadith-teaching pairs by consulting sources of reputable religious experts. This study presents the methodology of creating the dataset, which we make available on our repository, and discusses the models' performance that calls for the imminent need to explore avenues for improving the quality of these models to capture the semantics in such complex, low-resource texts.

null

24,564

inproceedings

britton-etal-2022-question

Question Modifiers in Visual Question Answering

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.158/

Britton, William and Sarkhel, Somdeb and Venugopal, Deepak

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1472--1479

Visual Question Answering (VQA) is a challenge problem that can advance AI by integrating several important sub-disciplines including natural language understanding and computer vision. Large VQA datasets that are publicly available for training and evaluation have driven the growth of VQA models that have obtained increasingly larger accuracy scores. However, it is also important to understand how much a model understands the details that are provided in a question. For example, studies in psychology have shown that syntactic complexity places a larger cognitive load on humans. Analogously, we want to understand if models have the perceptual capability to handle modifications to questions. Therefore, we develop a new dataset using Amazon Mechanical Turk where we asked workers to add modifiers to questions based on object properties and spatial relationships. We evaluate this data on LXMERT which is a state-of-the-art model in VQA that focuses more extensively on language processing. Our conclusions indicate that there is a significant negative impact on the performance of the model when the questions are modified to include more detailed information.

null

24,565

inproceedings

sosa-sharoff-2022-multimodal

Multimodal Pipeline for Collection of Misinformation Data from Telegram

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.159/

Sosa, Jose and Sharoff, Serge

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1480--1489

The paper presents the outcomes of AI-COVID19, our project aimed at better understanding of misinformation flow about COVID-19 across social media platforms. The specific focus of the study reported in this paper is on collecting data from Telegram groups which are active in promotion of COVID-related misinformation. Our corpus collected so far contains around 28 million words, from almost one million messages. Given that a substantial portion of misinformation flow in social media is spread via multimodal means, such as images and video, we have also developed a mechanism for utilising such channels via producing automatic transcripts for videos and automatic classification for images into such categories as memes, screenshots of posts and other kinds of images. The accuracy of the image classification pipeline is around 87{\%}.

null

24,566

inproceedings

xia-etal-2022-identifying

Identifying Tension in Holocaust Survivors' Interview: Code-switching/Code-mixing as Cues

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.160/

Xia, Xinyuan and Xiao, Lu and Yang, Kun and Wang, Yueyue

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1490--1495

In this study, we thrive on finding out how code-switching and code-mixing (CS/CM) as a linguistic phenomenon could be a sign of tension in Holocaust survivors' interviews. We first created an interview corpus (a total of 39 interviews) that contains manually annotated CS/CM codes (a total of 802 quotations). We then compared our annotations with the tension places in the corpus. The tensions are identified by a computational tool. We found that most of our annotations were captured in the tension places, and it showed a relatively outstanding performance. The finding implies that CS/CM can be appropriate cues for detecting tension in this communication context. Our CS/CM annotated interview corpus is openly accessible. Aside from annotating and examining CS/CM occurrences, we annotated silence situations in this open corpus. Silence is shown to be an indicator of tension in interpersonal communications. Making this corpus openly accessible, we call for more research endeavors on tension detection.

null

24,567

inproceedings

jensen-plank-2022-fine

Fine-tuning vs From Scratch: Do Vision {\&} Language Models Have Similar Capabilities on Out-of-Distribution Visual Question Answering?

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.161/

Jensen, Kristian N{\o}rgaard and Plank, Barbara

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1496--1508

Fine-tuning general-purpose pre-trained models has become a de-facto standard, also for Vision and Language tasks such as Visual Question Answering (VQA). In this paper, we take a step back and ask whether a fine-tuned model has superior linguistic and reasoning capabilities than a prior state-of-the-art architecture trained from scratch on the training data alone. We perform a fine-grained evaluation on out-of-distribution data, including an analysis on robustness due to linguistic variation (rephrasings). Our empirical results confirm the benefit of pre-training on overall performance and rephrasing in particular. But our results also uncover surprising limitations, particularly for answering questions involving boolean operations. To complement the empirical evaluation, this paper also surveys relevant earlier work on 1) available VQA data sets, 2) models developed for VQA, 3) pre-trained Vision+Language models, and 4) earlier fine-grained evaluation of pre-trained Vision+Language models.

null

24,568

inproceedings

koeva-etal-2022-multilingual

Multilingual Image Corpus {--} Towards a Multimodal and Multilingual Dataset

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.162/

Koeva, Svetla and Stoyanova, Ivelina and Kralev, Jordan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1509--1518

One of the processing tasks for large multimodal data streams is automatic image description (image classification, object segmentation and classification). Although the number and the diversity of image datasets is constantly expanding, still there is a huge demand for more datasets in terms of variety of domains and object classes covered. The goal of the project Multilingual Image Corpus (MIC 21) is to provide a large image dataset with annotated objects and object descriptions in 24 languages. The Multilingual Image Corpus consists of an Ontology of visual objects (based on WordNet) and a collection of thematically related images whose objects are annotated with segmentation masks and labels describing the ontology classes. The dataset is designed both for image classification and object detection and for semantic segmentation. The main contributions of our work are: a) the provision of large collection of high quality copyright-free images; b) the formulation of the Ontology of visual objects based on WordNet noun hierarchies; c) the precise manual correction of automatic object segmentation within the images and the annotation of object classes; and d) the association of objects and images with extended multilingual descriptions based on WordNet inner- and interlingual relations. The dataset can be used also for multilingual image caption generation, image-to-text alignment and automatic question answering for images and videos.

null

24,569

inproceedings

kim-etal-2022-sign

Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.163/

Kim, Jung-Ho and Hwang, Eui Jun and Cho, Sukmin and Lee, Du Hui and Park, Jong

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1519--1528

Sign language production (SLP) is the process of generating sign language videos from spoken language expressions. Since sign languages are highly under-resourced, existing vision-based SLP approaches suffer from out-of-vocabulary (OOV) and test-time generalization problems and thus generate low-quality translations. To address these problems, we introduce an avatar-based SLP system composed of a sign language translation (SLT) model and an avatar animation generation module. Our Transformer-based SLT model utilizes two additional strategies to resolve these problems: named entity transformation to reduce OOV tokens and context vector generation using a pretrained language model (e.g., BERT) to reliably train the decoder. Our system is validated on a new Korean-Korean Sign Language (KSL) dataset of weather forecasts and emergency announcements. Our SLT model achieves an 8.77 higher BLEU-4 score and a 4.57 higher ROUGE-L score over those of our baseline model. In a user evaluation, 93.48{\%} of named entities were successfully identified by participants, demonstrating marked improvement on OOV issues.

null

24,570

inproceedings

krishnaswamy-etal-2022-voxworld

The {V}ox{W}orld Platform for Multimodal Embodied Agents

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.164/

Krishnaswamy, Nikhil and Pickard, William and Cates, Brittany and Blanchard, Nathaniel and Pustejovsky, James

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1529--1541

We present a five-year retrospective on the development of the VoxWorld platform, first introduced as a multimodal platform for modeling motion language, that has evolved into a platform for rapidly building and deploying embodied agents with contextual and situational awareness, capable of interacting with humans in multiple modalities, and exploring their environments. In particular, we discuss the evolution from the theoretical underpinnings of the VoxML modeling language to a platform that accommodates both neural and symbolic inputs to build agents capable of multimodal interaction and hybrid reasoning. We focus on three distinct agent implementations and the functionality needed to accommodate all of them: Diana, a virtual collaborative agent; Kirby, a mobile robot; and BabyBAW, an agent who self-guides its own exploration of the world.

null

24,571

inproceedings

hossain-etal-2022-memosen

{M}emo{S}en: A Multimodal Dataset for Sentiment Analysis of Memes

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.165/

Hossain, Eftekhar and Sharif, Omar and Hoque, Mohammed Moshiul

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1542--1554

Posting and sharing memes have become a powerful expedient of expressing opinions on social media in recent days. Analysis of sentiment from memes has gained much attention to researchers due to its substantial implications in various domains like finance and politics. Past studies on sentiment analysis of memes have primarily been conducted in English, where low-resource languages gain little or no attention. However, due to the proliferation of social media usage in recent years, sentiment analysis of memes is also a crucial research issue in low resource languages. The scarcity of benchmark datasets is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. This paper presents a novel multimodal dataset (named MemoSen) for Bengali containing 4417 memes with three annotated labels positive, negative, and neutral. A detailed annotation guideline is provided to facilitate further resource development in this domain. Additionally, a set of experiments are carried out on MemoSen by constructing twelve unimodal (i.e., visual, textual) and ten multimodal (image+text) models. The evaluation exhibits that the integration of multimodal information significantly improves (about 1.2{\%}) the meme sentiment classification compared to the unimodal counterparts and thus elucidate the novel aspects of multimodality.

null

24,572

inproceedings

ivanko-etal-2022-rusavic

{RUSAVIC} Corpus: {R}ussian Audio-Visual Speech in Cars

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.166/

Ivanko, Denis and Axyonov, Alexandr and Ryumin, Dmitry and Kashevnik, Alexey and Karpov, Alexey

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1555--1559

We present a new audio-visual speech corpus (RUSAVIC) recorded in a car environment and designed for noise-robust speech recognition. Our goal was to produce a speech corpus which is natural (recorded in real driving conditions), controlled (providing different SNR levels by windows open/closed, moving/parked vehicle, etc.), and adequate size (the amount of data is enough to train state-of-the-art NN approaches). We focus on the problem of audio-visual speech recognition: with the use of automated lip-reading to improve the performance of audio-based speech recognition in the presence of severe acoustic noise caused by road traffic. We also describe the equipment and procedures used to create RUSAVIC corpus. Data are collected in a synchronous way through several smartphones located at different angles and equipped with FullHD video camera and microphone. The corpus includes the recordings of 20 drivers with minimum of 10 recording sessions for each. Besides providing a detailed description of the dataset and its collection pipeline, we evaluate several popular audio and visual speech recognition methods and present a set of baseline recognition results. At the moment RUSAVIC is a unique audio-visual corpus for the Russian language that is recorded in-the-wild condition and we make it publicly available.

null

24,573

inproceedings

challant-filhol-2022-first

A First Corpus of {AZ}ee Discourse Expressions

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.167/

Challant, Camille and Filhol, Michael

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1560--1565

This paper presents a corpus of AZee discourse expressions, i.e. expressions which formally describe Sign Language utterances of any length using the AZee approach and language. The construction of this corpus had two main goals: a first reference corpus for AZee, and a test of its coverage on a significant sample of real-life utterances. We worked on productions from an existing corpus, namely the {\textquotedblleft}40 breves{\textquotedblright}, containing an hour of French Sign Language. We wrote the corresponding AZee discourse expressions for the entire video content, i.e. expressions capturing the forms produced by the signers and their associated meaning by combining known production rules, a basic building block for these expressions. These are made available as a version 2 extension of the {\textquotedblleft}40 breves{\textquotedblright}. We explain the way in which these expressions can be built, present the resulting corpus and set of production rules used, and perform first measurements on it. We also propose an evaluation of our corpus: for one hour of discourse, AZee allows to describe 94{\%} of it, while ongoing studies are increasing this coverage. This corpus offers a lot of future prospects, for instance concerning synthesis with virtual signers, machine translation or formal grammars for Sign Language.

null

24,574

inproceedings

lebron-etal-2022-bertha

{BERTHA}: Video Captioning Evaluation Via Transfer-Learned Human Assessment

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.168/

Lebron, Luis and Graham, Yvette and McGuinness, Kevin and Kouramas, Konstantinos and O{'}Connor, Noel E.

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1566--1575

Evaluating video captioning systems is a challenging task as there are multiple factors to consider; for instance: the fluency of the caption, multiple actions happening in a single scene, and the human bias of what is considered important. Most metrics try to measure how similar the system generated captions are to a single or a set of human-annotated captions. This paper presents a new method based on a deep learning model to evaluate these systems. The model is based on BERT, which is a language model that has been shown to work well in multiple NLP tasks. The aim is for the model to learn to perform an evaluation similar to that of a human. To do so, we use a dataset that contains human evaluations of system generated captions. The dataset consists of the human judgments of the captions produces by the system participating in various years of the TRECVid video to text task. BERTHA obtain favourable results, outperforming the commonly used metrics in some setups.

null

24,575

inproceedings

brutti-etal-2022-abstract

{A}bstract {M}eaning {R}epresentation for Gesture

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.169/

Brutti, Richard and Donatelli, Lucia and Lai, Kenneth and Pustejovsky, James

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1576--1583

This paper presents Gesture AMR, an extension to Abstract Meaning Representation (AMR), that captures the meaning of gesture. In developing Gesture AMR, we consider how gesture form and meaning relate; how gesture packages meaning both independently and in interaction with speech; and how the meaning of gesture is temporally and contextually determined. Our case study for developing Gesture AMR is a focused human-human shared task to build block structures. We develop an initial taxonomy of gesture act relations that adheres to AMR`s existing focus on predicate-argument structure while integrating meaningful elements unique to gesture. Pilot annotation shows Gesture AMR to be more challenging than standard AMR, and illustrates the need for more work on representation of dialogue and multimodal meaning. We discuss challenges of adapting an existing meaning representation to non-speech-based modalities and outline several avenues for expanding Gesture AMR.

null

24,576

inproceedings

kuzman-etal-2022-ginco

The {GINCO} Training Dataset for Web Genre Identification of Documents Out in the Wild

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.170/

Kuzman, Taja and Rupnik, Peter and Ljube{\v{s}}i{\'c}, Nikola

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1584--1594

This paper presents a new training dataset for automatic genre identification GINCO, which is based on 1,125 crawled Slovenian web documents that consist of 650,000 words. Each document was manually annotated for genre with a new annotation schema that builds upon existing schemata, having primarily clarity of labels and inter-annotator agreement in mind. The dataset consists of various challenges related to web-based data, such as machine translated content, encoding errors, multiple contents presented in one document etc., enabling evaluation of classifiers in realistic conditions. The initial machine learning experiments on the dataset show that (1) pre-Transformer models are drastically less able to model the phenomena, with macro F1 metrics ranging around 0.22, while Transformer-based models achieve scores of around 0.58, and (2) multilingual Transformer models work as well on the task as the monolingual models that were previously proven to be superior to multilingual models on standard NLP tasks.

null

24,577

inproceedings

laperriere-etal-2022-spoken

The Spoken Language Understanding {MEDIA} Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.171/

Laperri{\`ere, Ga{\"elle and Pelloin, Valentin and Caubri{\`ere, Antoine and Mdhaffar, Salima and Camelin, Nathalie and Ghannay, Sahar and Jabaian, Bassam and Est{\`eve, Yannick

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1595--1602

With the emergence of neural end-to-end approaches for spoken language understanding (SLU), a growing number of studies have been presented during these last three years on this topic. The major part of these works addresses the spoken language understanding domain through a simple task like speech intent detection. In this context, new benchmark datasets have also been produced and shared with the community related to this task. In this paper, we focus on the French MEDIA SLU dataset, distributed since 2005 and used as a benchmark dataset for a large number of research works. This dataset has been shown as being the most challenging one among those accessible to the research community. Distributed by ELRA, this corpus is free for academic research since 2019. Unfortunately, the MEDIA dataset is not really used beyond the French research community. To facilitate its use, a complete recipe, including data preparation, training and evaluation scripts, has been built and integrated to SpeechBrain, an already popular open-source and all-in-one conversational AI toolkit based on PyTorch. This recipe is presented in this paper. In addition, based on the feedback of some researchers who have worked on this dataset for several years, some corrections have been brought to the initial manual annotation: the new version of the data will also be integrated into the ELRA catalogue, as the original one. More, a significant amount of data collected during the construction of the MEDIA corpus in the 2000s was never used until now: we present the first results reached on this subset {---} also included in the MEDIA SpeechBrain recipe {---} , that will be used for now as the MEDIA test2. Last, we discuss evaluation issues.

null

24,578

inproceedings

urbizu-etal-2022-basqueglue

{B}asque{GLUE}: A Natural Language Understanding Benchmark for {B}asque

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.172/

Urbizu, Gorka and San Vicente, I{\~n}aki and Saralegi, Xabier and Agerri, Rodrigo and Soroa, Aitor

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1603--1612

Natural Language Understanding (NLU) technology has improved significantly over the last few years and multitask benchmarks such as GLUE are key to evaluate this improvement in a robust and general way. These benchmarks take into account a wide and diverse set of NLU tasks that require some form of language understanding, beyond the detection of superficial, textual clues. However, they are costly to develop and language-dependent, and therefore they are only available for a small number of languages. In this paper, we present BasqueGLUE, the first NLU benchmark for Basque, a less-resourced language, which has been elaborated from previously existing datasets and following similar criteria to those used for the construction of GLUE and SuperGLUE. We also report the evaluation of two state-of-the-art language models for Basque on BasqueGLUE, thus providing a strong baseline to compare upon. BasqueGLUE is freely available under an open license.

null

24,579

inproceedings

stefanovitch-etal-2022-resources

Resources and Experiments on Sentiment Classification for {G}eorgian

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.173/

Stefanovitch, Nicolas and Piskorski, Jakub and Kharazi, Sopho

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1613--1621

This paper presents, to the best of our knowledge, the first ever publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. The characteristics of these resources and the process of their creation are described in detail. The results of various experiments on the performance of both lexicon- and machine learning-based models for Georgian sentiment classification are also reported. Both 3-label (positive, neutral, negative) and 4-label settings (same labels + mixed) are considered. The machine learning models explored include, i.a., logistic regression, SVMs, and transformed-based models. We also explore transfer learning- and translation-based (to a well-supported language) approaches. The obtained results for Georgian are on par with the state-of-the-art results in sentiment classification for well studied languages when using training data of comparable size.

null

24,580

inproceedings

zmandar-etal-2022-cofif

{C}o{F}i{F} Plus: A {F}rench Financial Narrative Summarisation Corpus

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.174/

Zmandar, Nadhem and Daudert, Tobias and Ahmadi, Sina and El-Haj, Mahmoud and Rayson, Paul

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1622--1639

Natural Language Processing is increasingly being applied in the finance and business industry to analyse the text of many different types of financial documents. Given the increasing growth of firms around the world, the volume of financial disclosures and financial texts in different languages and forms is increasing sharply and therefore the study of language technology methods that automatically summarise content has grown rapidly into a major research area. Corpora for financial narrative summarisation exists in English, but there is a significant lack of financial text resources in the French language. To remedy this, we present CoFiF Plus, the first French financial narrative summarisation dataset providing a comprehensive set of financial text written in French. The dataset has been extracted from French financial reports published in PDF file format. It is composed of 1,703 reports from the most capitalised companies in France (Euronext Paris) covering a time frame from 1995 to 2021. This paper describes the collection, annotation and validation of the financial reports and their summaries. It also describes the dataset and gives the results of some baseline summarisers. Our datasets will be openly available upon the acceptance of the paper.

null

24,581

inproceedings

calizzano-etal-2022-generating

Generating Extended and Multilingual Summaries with Pre-trained Transformers

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.175/

Calizzano, R{\'e}mi and Ostendorff, Malte and Ruan, Qian and Rehm, Georg

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1640--1650

Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisation tailored for extended summaries of approx. 11 sentences. The dataset comprises 39,626 summaries which are news articles from Wikinews and their sources. We compare three multilingual transformer models on the extractive summarisation task and three training scenarios on which we fine-tune mT5 to perform abstractive summarisation. This results in strong baselines for both extractive and abstractive summarisation on WikinewsSum. We also show how the combination of an extractive model with an abstractive one can be used to create extended abstractive summaries from long input documents. Finally, our results show that fine-tuning mT5 on all the languages combined significantly improves the summarisation performance on low-resource languages.

null

24,582

inproceedings

martin-etal-2022-muss

{MUSS}: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.176/

Martin, Louis and Fan, Angela and de la Clergerie, {\'E}ric and Bordes, Antoine and Sagot, Beno{\^i}t

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1651--1664

Progress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English. We introduce MUSS, a Multilingual Unsupervised Sentence Simplification system that does not require labeled simplification data. MUSS uses a novel approach to sentence simplification that trains strong models using sentence-level paraphrase data instead of proper simplification data. These models leverage unsupervised pretraining and controllable generation mechanisms to flexibly adjust attributes such as length and lexical complexity at inference time. We further present a method to mine such paraphrase data in any language from Common Crawl using semantic sentence embeddings, thus removing the need for labeled data. We evaluate our approach on English, French, and Spanish simplification benchmarks and closely match or outperform the previous best supervised results, despite not using any labeled simplification data. We push the state of the art further by incorporating labeled simplification data.

null

24,583

inproceedings

honnavalli-etal-2022-towards

Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.177/

Honnavalli, Samhita and Parekh, Aesha and Ou, Lily and Groenwold, Sophie and Levy, Sharon and Ordonez, Vicente and Wang, William Yang

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1665--1670

Women are often perceived as junior to their male counterparts, even within the same job titles. While there has been significant progress in the evaluation of gender bias in natural language processing (NLP), existing studies seldom investigate how biases toward gender groups change when compounded with other societal biases. In this work, we investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models by introducing a novel framework for probing compound bias. We contribute a benchmark robustness-testing dataset spanning two domains, U.S. senatorship and professorship, created using a distant-supervision method. Our dataset includes human-written text with underlying ground truth and paired counterfactuals. We then examine GPT-2 perplexity and the frequency of gendered language in generated text. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.

null

24,584

inproceedings

tamburini-2022-combining

Combining {ELECTRA} and Adaptive Graph Encoding for Frame Identification

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.178/

Tamburini, Fabio

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1671--1679

This paper presents contributions in two directions: first we propose a new system for Frame Identification (FI), based on pre-trained text encoders trained discriminatively and graphs embedding, producing state of the art performance and, second, we take in consideration all the extremely different procedures used to evaluate systems for this task performing a complete evaluation over two benchmarks and all possible splits and cleaning procedures used in the FI literature.

null

24,585

inproceedings

gari-soler-etal-2022-polysemy

Polysemy in Spoken Conversations and Written Texts

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.179/

Gar{\'i} Soler, Aina and Labeau, Matthieu and Clavel, Chlo{\'e}

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1680--1690

Our discourses are full of potential lexical ambiguities, due in part to the pervasive use of words having multiple senses. Sometimes, one word may even be used in more than one sense throughout a text. But, to what extent is this true for different kinds of texts? Does the use of polysemous words change when a discourse involves two people, or when speakers have time to plan what to say? We investigate these questions by comparing the polysemy level of texts of different nature, with a focus on spontaneous spoken dialogs; unlike previous work which examines solely scripted, written, monolog-like data. We compare multiple metrics that presuppose different conceptualizations of text polysemy, i.e., they consider the observed or the potential number of senses of words, or their sense distribution in a discourse. We show that the polysemy level of texts varies greatly depending on the kind of text considered, with dialog and spoken discourses having generally a higher polysemy level than written monologs. Additionally, our results emphasize the need for relaxing the popular {\textquotedblleft}one sense per discourse{\textquotedblright} hypothesis.

null

24,586

inproceedings

batanovic-milicevic-petrovic-2022-cross

Cross-Level Semantic Similarity for {S}erbian Newswire Texts

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.180/

Batanovi{\'c}, Vuk and Mili{\v{c}}evi{\'c} Petrovi{\'c}, Maja

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1691--1699

Cross-Level Semantic Similarity (CLSS) is a measure of the level of semantic overlap between texts of different lengths. Although this problem was formulated almost a decade ago, research on it has been sparse, and limited exclusively to the English language. In this paper, we present the first CLSS dataset in another language, in the form of CLSS.news.sr {--} a corpus of 1000 phrase-sentence and 1000 sentence-paragraph newswire text pairs in Serbian, manually annotated with fine-grained semantic similarity scores using a 0{--}4 similarity scale. We describe the methodology of data collection and annotation, and compare the resulting corpus to its preexisting counterpart in English, SemEval CLSS, following up with a preliminary linguistic analysis of the newly created dataset. State-of-the-art pre-trained language models are then fine-tuned and evaluated on the CLSS task in Serbian using the produced data, and their settings and results are discussed. The CLSS.news.sr corpus and the guidelines used in its creation are made publicly available.

null

24,587

inproceedings

jindal-etal-2022-universal

Universal {P}roposition {B}ank 2.0

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.181/

Jindal, Ishan and Rademaker, Alexandre and Ulewicz, Micha{\l} and Linh, Ha and Nguyen, Huyen and Tran, Khoi-Nguyen and Zhu, Huaiyu and Li, Yunyao

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1700--1711

Semantic role labeling (SRL) represents the meaning of a sentence in the form of predicate-argument structures. Such shallow semantic analysis is helpful in a wide range of downstream NLP tasks and real-world applications. As treebanks enabled the development of powerful syntactic parsers, the accurate predicate-argument analysis demands training data in the form of propbanks. Unfortunately, most languages simply do not have corresponding propbanks due to the high cost required to construct such resources. To overcome such challenges, Universal Proposition Bank 1.0 (UP1.0) was released in 2017, with high-quality propbank data generated via a two-stage method exploiting monolingual SRL and multilingual parallel data. In this paper, we introduce Universal Proposition Bank 2.0 (UP2.0), with significant enhancements over UP1.0: (1) propbanks with higher quality by using a state-of-the-art monolingual SRL and improved auto-generation of annotations; (2) expanded language coverage (from 7 to 9 languages); (3) span annotation for the decoupling of syntactic analysis; and (4) Gold data for a subset of the languages. We also share our experimental results that confirm the significant quality improvements of the generated propbanks. In addition, we present a comprehensive experimental evaluation on how different implementation choices impact the quality of the resulting data. We release these resources to the research community and hope to encourage more research on cross-lingual SRL.

null

24,588

inproceedings

hollenstein-etal-2022-copenhagen

The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of {D}anish Texts

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.182/

Hollenstein, Nora and Barrett, Maria and Bj{\"ornsd{\'ottir, Marina

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1712--1720

Eye movement recordings from reading are one of the richest signals of human language processing. Corpora of eye movements during reading of contextualized running text is a way of making such records available for natural language processing purposes. Such corpora already exist in some languages. We present CopCo, the Copenhagen Corpus of eye tracking recordings from natural reading of Danish texts. It is the first eye tracking corpus of its kind for the Danish language. CopCo includes 1,832 sentences with 34,897 tokens of Danish text extracted from a collection of speech manuscripts. This first release of the corpus contains eye tracking data from 22 participants. It will be extended continuously with more participants and texts from other genres. We assess the data quality of the recorded eye movements and find that the extracted features are in line with related research. The dataset available here: \url{https://osf.io/ud8s5/}.

null

24,589

inproceedings

weise-etal-2022-brooklyn

The Brooklyn Multi-Interaction Corpus for Analyzing Variation in Entrainment Behavior

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.183/

Weise, Andreas and McNeill, Matthew and Levitan, Rivka

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1721--1731

We present the Brooklyn Multi-Interaction Corpus (B-MIC), a collection of dyadic conversations designed to identify speaker traits and conversation contexts that cause variations in entrainment behavior. B-MIC pairs each participant with multiple partners for an object placement game and open-ended discussions, as well as with a Wizard of Oz for a baseline of their speech. In addition to fully transcribed recordings, it includes demographic information and four completed psychological questionnaires for each subject and turn annotations for perceived emotion and acoustic outliers. This enables the study of speakers' entrainment behavior in different contexts and the sources of variation in this behavior. In this paper, we introduce B-MIC and describe our collection, annotation, and preprocessing methodologies. We report a preliminary study demonstrating varied entrainment behavior across different conversation types and discuss the rich potential for future work on the corpus.

null

24,590

inproceedings

miletic-etal-2022-pro

Pro-{TEXT}: an Annotated Corpus of Keystroke Logs

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.184/

Miletic, Aleksandra and Benzitoun, Christophe and Cislaru, Georgeta and Herrera-Yanez, Santiago

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1732--1739

Pro-TEXT is a corpus of keystroke logs written in French. Keystroke logs are recordings of the writing process executed through a keyboard, which keep track of all actions taken by the writer (character additions, deletions, substitutions). As such, the Pro-TEXT corpus offers new insights into text genesis and underlying cognitive processes from the production perspective. A subset of the corpus is linguistically annotated with parts of speech, lemmas and syntactic dependencies, making it suitable for the study of interactions between linguistic and behavioural aspects of the writing process. The full corpus contains 202K tokens, while the annotated portion is currently 30K tokens large. The annotated content is progressively being made available in a database-like CSV format and in CoNLL format, and the work on an HTML-based visualisation tool is currently under way. To the best of our knowledge, Pro-TEXT is the first corpus of its kind in French.

null

24,591

inproceedings

bonetti-etal-2022-work

Work Hard, Play Hard: Collecting Acceptability Annotations through a 3{D} Game

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.185/

Bonetti, Federico and Leonardelli, Elisa and Trotta, Daniela and Guarasci, Raffaele and Tonelli, Sara

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1740--1750

Corpus-based studies on acceptability judgements have always stimulated the interest of researchers, both in theoretical and computational fields. Some approaches focused on spontaneous judgements collected through different types of tasks, others on data annotated through crowd-sourcing platforms, still others relied on expert annotated data available from the literature. The release of CoLA corpus, a large-scale corpus of sentences extracted from linguistic handbooks as examples of acceptable/non acceptable phenomena in English, has revived interest in the reliability of judgements of linguistic experts vs. non-experts. Several issues are still open. In this work, we contribute to this debate by presenting a 3D video game that was used to collect acceptability judgments on Italian sentences. We analyse the resulting annotations in terms of agreement among players and by comparing them with experts' acceptability judgments. We also discuss different game settings to assess their impact on participants' motivation and engagement. The final dataset containing 1,062 sentences, which were selected based on majority voting, is released for future research and comparisons.

null

24,592

inproceedings

lapshinova-koltunski-etal-2022-dihutra-parallel

{D}i{H}u{T}ra: a Parallel Corpus to Analyse Differences between Human Translations

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.186/

Lapshinova-Koltunski, Ekaterina and Popovi{\'c}, Maja and Koponen, Maarit

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1751--1760

This paper describes a new corpus of human translations which contains both professional and students translations. The data consists of English sources {--} texts from news and reviews {--} and their translations into Russian and Croatian, as well as of the subcorpus containing translations of the review texts into Finnish. All target languages represent mid-resourced and less or mid-investigated ones. The corpus will be valuable for studying variation in translation as it allows a direct comparison between human translations of the same source texts. The corpus will also be a valuable resource for evaluating machine translation systems. We believe that this resource will facilitate understanding and improvement of the quality issues in both human and machine translation. In the paper, we describe how the data was collected, provide information on translator groups and summarise the differences between the human translations at hand based on our preliminary results with shallow features.

null

24,593

inproceedings

jahan-etal-2022-data

Data Expansion Using {W}ord{N}et-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.187/

Jahan, Md Saroar and Beddiar, Djamila Romaissa and Oussalah, Mourad and Mohamed, Muhidin

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1761--1770

Automatic identification of cyberbullying from textual content is known to be a challenging task. The challenges arise from the inherent structure of cyberbullying and the lack of labeled large-scale corpus, enabling efficient machine-learning-based tools including neural networks. This paper advocates a data augmentation-based approach that could enhance the automatic detection of cyberbullying in social media texts. We use both word sense disambiguation and synonymy relation in WordNet lexical database to generate coherent equivalent utterances of cyberbullying input data. The disambiguation and semantic expansion are intended to overcome the inherent limitations of social media posts, such as an abundance of unstructured constructs and limited semantic content. Besides, to test the feasibility, a novel protocol has been employed to collect cyberbullying traces data from AskFm forum, where about a 10K-size dataset has been manually labeled. Next, the problem of cyberbullying identification is viewed as a binary classification problem using an elaborated data augmentation strategy and an appropriate classifier. For the latter, a Convolutional Neural Network (CNN) architecture with FastText and BERT was put forward, whose results were compared against commonly employed Na{\"ive Bayes (NB) and Logistic Regression (LR) classifiers with and without data augmentation. The research outcomes were promising and yielded almost 98.4{\% of classifier accuracy, an improvement of more than 4{\% over baseline results.

null

24,594

inproceedings

polak-etal-2022-alignmeet

{ALIGNMEET}: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.188/

Pol{\'a}k, Peter and Singh, Muskaan and Nedoluzhko, Anna and Bojar, Ond{\v{r}}ej

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1771--1779

Summarization is a challenging problem, and even more challenging is to manually create, correct, and evaluate the summaries. The severity of the problem grows when the inputs are multi-party dialogues in a meeting setup. To facilitate the research in this area, we present ALIGNMEET, a comprehensive tool for meeting annotation, alignment, and evaluation. The tool aims to provide an efficient and clear interface for fast annotation while mitigating the risk of introducing errors. Moreover, we add an evaluation mode that enables a comprehensive quality evaluation of meeting minutes. To the best of our knowledge, there is no such tool available. We release the tool as open source. It is also directly installable from PyPI.

null

24,595

inproceedings

bayerl-etal-2022-ksof

{KS}o{F}: The Kassel State of Fluency Dataset {--} A Therapy Centered Dataset of Stuttering

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.189/

Bayerl, Sebastian and Wolff von Gudenberg, Alexander and H{\"onig, Florian and Noeth, Elmar and Riedhammer, Korbinian

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1780--1787

Stuttering is a complex speech disorder that negatively affects an individual`s ability to communicate effectively. Persons who stutter (PWS) often suffer considerably under the condition and seek help through therapy. Fluency shaping is a therapy approach where PWSs learn to modify their speech to help them to overcome their stutter. Mastering such speech techniques takes time and practice, even after therapy. Shortly after therapy, success is evaluated highly, but relapse rates are high. To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency. Monitoring could create the ability to intervene early by detecting lapses in fluency. To the best of our knowledge, no public dataset is available that contains speech from people who underwent stuttering therapy that changed the style of speaking. This work introduces the Kassel State of Fluency (KSoF), a therapy-based dataset containing over 5500 clips of PWSs. The clips were labeled with six stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and {--} specific to therapy {--} speech modifications. The audio was recorded during therapy sessions at the Institut der Kasseler Stottertherapie. The data will be made available for research purposes upon request.

null

24,596

inproceedings

guibon-etal-2022-ezcat

{EZCAT}: an Easy Conversation Annotation Tool

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.190/

Guibon, Ga{\"el and Lefeuvre, Luce and Labeau, Matthieu and Clavel, Chlo{\'e

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1788--1797

Users generate content constantly, leading to new data requiring annotation. Among this data, textual conversations are created every day and come with some specificities: they are mostly private through instant messaging applications, requiring the conversational context to be labeled. These specificities led to several annotation tools dedicated to conversation, and mostly dedicated to dialogue tasks, requiring complex annotation schemata, not always customizable and not taking into account conversation-level labels. In this paper, we present EZCAT, an easy-to-use interface to annotate conversations in a two-level configurable schema, leveraging message-level labels and conversation-level labels. Our interface is characterized by the voluntary absence of a server and accounts management, enhancing its availability to anyone, and the control over data, which is crucial to confidential conversations. We also present our first usage of EZCAT along with our annotation schema we used to annotate confidential customer service conversations. EZCAT is freely available at \url{https://gguibon.github.io/ezcat}.

null

24,597

inproceedings

dobrovoljc-2022-spoken

Spoken Language Treebanks in {U}niversal {D}ependencies: an Overview

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.191/

Dobrovoljc, Kaja

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1798--1806

Given the benefits of syntactically annotated collections of transcribed speech in spoken language research and applications, many spoken language treebanks have been developed in the last decades, with divergent annotation schemes posing important limitations to cross-resource explorations, such as comparing data across languages, grammatical frameworks, and language domains. As a consequence, there has been a growing number of spoken language treebanks adopting the Universal Dependencies (UD) annotation scheme, aimed at cross-linguistically consistent morphosyntactic annotation. In view of the non-central role of spoken language data within the scheme and with little in-domain consolidation to date, this paper presents a comparative overview of spoken language treebanks in UD to support cross-treebank data explorations on the one hand, and encourage further treebank harmonization on the other. Our results show that the spoken language treebanks differ considerably with respect to the inventory and the format of transcribed phenomena, as well as the principles adopted in their morphosyntactic annotation. This is particularly true for the dependency annotation of speech disfluencies, where conflicting data annotations suggest an underspecification of the guidelines pertaining to speech repairs in general and the reparandum dependency relation in particular.

null

24,598

inproceedings

vanroy-macken-2022-lecontra

{L}e{C}on{T}ra: A Learner Corpus of {E}nglish-to-{D}utch News Translation

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.192/

Vanroy, Bram and Macken, Lieve

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1807--1816

We present LeConTra, a learner corpus consisting of English-to-Dutch news translations enriched with translation process data. Three students of a Master`s programme in Translation were asked to translate 50 different English journalistic texts of approximately 250 tokens each. Because we also collected translation process data in the form of keystroke logging, our dataset can be used as part of different research strands such as translation process research, learner corpus research, and corpus-based translation studies. Reference translations, without process data, are also included. The data has been manually segmented and tokenized, and manually aligned at both segment and word level, leading to a high-quality corpus with token-level process data. The data is freely accessible via the Translation Process Research DataBase, which emphasises our commitment of distributing our dataset. The tool that was built for manual sentence segmentation and tokenization, Mantis, is also available as an open-source aid for data processing.

null

24,599

inproceedings

hladka-etal-2022-annotating

Annotating Attribution in {C}zech News Server Articles

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.193/

Hladka, Barbora and M{\'i}rovsk{\'y}, Ji{\v{r}}{\'i} and Kopp, Maty{\'a}{\v{s}} and Moravec, V{\'a}clav

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1817--1823

This paper focuses on detection of sources in the Czech articles published on a news server of Czech public radio. In particular, we search for attribution in sentences and we recognize attributed sources and their sentence context (signals). We organized a crowdsourcing annotation task that resulted in a data set of 2,167 stories with manually recognized signals and sources. In addition, the sources were classified into the classes of named and unnamed sources.

null

24,600

inproceedings

gessler-etal-2022-xposition

Xposition: An Online Multilingual Database of Adpositional Semantics

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.194/

Gessler, Luke and Blodgett, Austin and Ledford, Joseph C. and Schneider, Nathan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1824--1830

We present Xposition, an online platform for documenting adpositional semantics across languages in terms of supersenses (Schneider et al., 2018). More than just a lexical database, Xposition houses annotation guidelines, structured lexicographic documentation, and annotated corpora. Guidelines and documentation are stored as wiki pages for ease of editing, and described elements (supersenses, adpositions, etc.) are hyperlinked for ease of browsing. We describe how the platform structures information; its current contents across several languages; and aspects of the design of the web application that supports it, with special attention to how it supports datasets and standards that evolve over time.

null

24,601

inproceedings

tracey-etal-2022-study

A Study in Contradiction: Data and Annotation for {AIDA} Focusing on Informational Conflict in {R}ussia-{U}kraine Relations

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.195/

Tracey, Jennifer and Bies, Ann and Getman, Jeremy and Griffitt, Kira and Strassel, Stephanie

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1831--1838

This paper describes data resources created for Phase 1 of the DARPA Active Interpretation of Disparate Alternatives (AIDA) program, which aims to develop language technology that can help humans manage large volumes of sometimes conflicting information to develop a comprehensive understanding of events around the world, even when such events are described in multiple media and languages. Especially important is the need for the technology to be capable of building multiple hypotheses to account for alternative interpretations of data imbued with informational conflict. The corpus described here is designed to support these goals. It focuses on the domain of Russia-Ukraine relations and contains multimedia source data in English, Russian and Ukrainian, annotated to support development and evaluation of systems that perform extraction of entities, events, and relations from individual multimedia documents, aggregate the information across documents and languages, and produce multiple {\textquotedblleft}hypotheses{\textquotedblright} about what has happened. This paper describes source data collection, annotation, and assessment.

null

24,602

inproceedings

hadj-mohamed-etal-2022-annotating

Annotating Verbal Multiword Expressions in {A}rabic: Assessing the Validity of a Multilingual Annotation Procedure

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.196/

Hadj Mohamed, Najet and Ben Khelil, Cherifa and Savary, Agata and Keskes, Iskandar and Antoine, Jean-Yves and Hadrich-Belguith, Lamia

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1839--1848

This paper describes our efforts to extend the PARSEME framework to Modern Standard Arabic. Theapplicability of the PARSEME guidelines was tested by measuring the inter-annotator agreement in theearly annotation stage. A subset of 1,062 sentences from the Prague Arabic Dependency Treebank PADTwas selected and annotated by two Arabic native speakers independently. Following their annotations, anew Arabic corpus with over 1,250 annotated VMWEs has been built. This corpus already exceeds thesmallest corpora of the PARSEME suite, and enables first observations. We discuss our annotation guide-line schema that shows full MWE annotation is realizable in Arabic where we get good inter-annotator agreement.

null

24,603

inproceedings

figueroa-etal-2022-annotation

Annotation of Communicative Functions of Short Feedback Tokens in Switchboard

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.197/

Figueroa, Carol and Adigwe, Adaeze and Ochs, Magalie and Skantze, Gabriel

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1849--1859

There has been a lot of work on predicting the timing of feedback in conversational systems. However, there has been less focus on predicting the prosody and lexical form of feedback given their communicative function. Therefore, in this paper we present our preliminary annotations of the communicative functions of 1627 short feedback tokens from the Switchboard corpus and an analysis of their lexical realizations and prosodic characteristics. Since there is no standard scheme for annotating the communicative function of feedback we propose our own annotation scheme. Although our work is ongoing, our preliminary analysis revealed lexical tokens such as {\textquotedblleft}yeah{\textquotedblright} are ambiguous and therefore lexical forms alone are not indicative of the function. Both the lexical form and prosodic characteristics need to be taken into account in order to predict the communicative function. We also found that feedback functions have distinguishable prosodic characteristics in terms of duration, mean pitch, pitch slope, and pitch range.

null

24,604

inproceedings

ajvazi-hardmeier-2022-dataset

A Dataset of Offensive Language in Kosovo Social Media

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.198/

Ajvazi, Adem and Hardmeier, Christian

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1860--1869

Social media are a central part of people`s lives. Unfortunately, many public social media spaces are rife with bullying and offensive language, creating an unsafe environment for their users. In this paper, we present a new dataset for offensive language detection in Albanian. The dataset is composed of user-generated comments on Facebook and YouTube from the channels of selected Kosovo news platforms. It is annotated according to the three levels of the OLID annotation scheme. We also show results of a baseline system for offensive language classification based on a fine-tuned BERT model and compare with the Danish DKhate dataset, which is similar in scope and size. In a transfer learning setting, we find that merging the Albanian and Danish training sets leads to improved performance for prediction on Danish, but not Albanian, on both offensive language recognition and distinguishing targeted and untargeted offence.

null

24,605

inproceedings

alhafni-etal-2022-arabic

The {A}rabic Parallel Gender Corpus 2.0: Extensions and Analyses

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.199/

Alhafni, Bashar and Habash, Nizar and Bouamor, Houda

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1870--1884

Gender bias in natural language processing (NLP) applications, particularly machine translation, has been receiving increasing attention. Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems. Addressing the problem in poorly resourced, and/or morphologically rich languages has lagged behind, largely due to the lack of datasets and resources. In this paper, we introduce a new corpus for gender identification and rewriting in contexts involving one or two target users (I and/or You) {--} first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. The corpus has multiple parallel components: four combinations of 1st and 2nd person in feminine and masculine grammatical genders, as well as English, and English to Arabic machine translation output. This corpus expands on Habash et al. (2019)`s Arabic Parallel Gender Corpus (APGC v1.0) by adding second person targets as well as increasing the total number of sentences over 6.5 times, reaching over 590K words. Our new dataset will aid the research and development of gender identification, controlled text generation, and post-editing rewrite systems that could be used to personalize NLP applications and provide users with the correct outputs based on their grammatical gender preferences. We make the Arabic Parallel Gender Corpus (APGC v2.0) publicly available

null

24,606

inproceedings

cheng-etal-2022-engage

The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.200/

Cheng, Daniel and Yan, Kyle and Keung, Phillip and Smith, Noah A.

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1885--1889

Social media platforms play an increasingly important role as forums for public discourse. Many platforms use recommendation algorithms that funnel users to online groups with the goal of maximizing user engagement, which many commentators have pointed to as a source of polarization and misinformation. Understanding the role of NLP in recommender systems is an interesting research area, given the role that social media has played in world events. However, there are few standardized resources which researchers can use to build models that predict engagement with online groups on social media; each research group constructs datasets from scratch without releasing their version for reuse. In this work, we present a dataset drawn from posts and comments on the online message board Reddit. We develop baseline models for recommending subreddits to users, given the user`s post and comment history. We also study the behavior of our recommender models on subreddits that were banned in June 2020 as part of Reddit`s efforts to stop the dissemination of hate speech.

null

24,607

inproceedings

rocha-etal-2022-annotating

Annotating Arguments in a Corpus of Opinion Articles

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.201/

Rocha, Gil and Trigo, Lu{\'i}s and Lopes Cardoso, Henrique and Sousa-Silva, Rui and Carvalho, Paula and Martins, Bruno and Won, Miguel

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1890--1899

Interest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or legal documents. Conversely, we report on the first extensive and consolidated Portuguese argument annotation project focused on opinion articles. We briefly describe the annotation guidelines based on a multi-layered process and analyze the manual annotations produced, highlighting the main challenges of this textual genre. We then conduct a comprehensive inter-annotator agreement analysis, including argumentative discourse units, their classes and relations, and resulting graphs. This analysis reveals that each of these aspects tackles very different kinds of challenges. We observe differences in annotator profiles, motivating our aim of producing a non-aggregated corpus containing the insights of every annotator. We note that the interpretation and identification of token-level arguments is challenging; nevertheless, tasks that focus on higher-level components of the argument structure can obtain considerable agreement. We lay down perspectives on corpus usage, exploiting its multi-faceted nature.

null

24,608

inproceedings

abrami-etal-2022-german

{G}erman Parliamentary Corpus ({G}er{P}ar{C}or)

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.202/

Abrami, Giuseppe and Bagci, Mevl{\"ut and Hammerla, Leon and Mehler, Alexander

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1900--1906

Parliamentary debates represent a large and partly unexploited treasure trove of publicly accessible texts. In the German-speaking area, there is a certain deficit of uniformly accessible and annotated corpora covering all German-speaking parliaments at the national and federal level. To address this gap, we introduce the German Parliamentary Corpus (GerParCor). GerParCor is a genre-specific corpus of (predominantly historical) German-language parliamentary protocols from three centuries and four countries, including state and federal level data. In addition, GerParCor contains conversions of scanned protocols and, in particular, of protocols in Fraktur converted via an OCR process based on Tesseract. All protocols were preprocessed by means of the NLP pipeline of spaCy3 and automatically annotated with metadata regarding their session date. GerParCor is made available in the XMI format of the UIMA project. In this way, GerParCor can be used as a large corpus of historical texts in the field of political communication for various tasks in NLP.

null

24,609

inproceedings

novak-novak-2022-nerkor

{N}er{K}or+{C}ars-{O}nto{N}otes++

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.203/

Nov{\'a}k, Attila and Nov{\'a}k, Borb{\'a}la

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1907--1916

In this paper, we present an upgraded version of the Hungarian NYTK-NerKor named entity corpus, which contains about twice as many annotated spans and 7 times as many distinct entity types as the original version. We used an extended version of the OntoNotes 5 annotation scheme including time and numerical expressions. NerKor is the newest and biggest NER corpus for Hungarian containing diverse domains. We applied cross-lingual transfer of NER models trained for other languages based on multilingual contextual language models to preannotate the corpus. We corrected the annotation semi-automatically and manually. Zero-shot preannotation was very effective with about 0.82 F1 score for the best model. We also added a 12000-token subcorpus on cars and other motor vehicles. We trained and release a transformer-based NER tagger for Hungarian using the annotation in the new corpus version, which provides similar performance to an identical model trained on the original version of the corpus.

null

24,610

inproceedings

burkhardt-etal-2022-comparative

A Comparative Cross Language View On Acted Databases Portraying Basic Emotions Utilising Machine Learning

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.204/

Burkhardt, Felix and Hacker, Anabell and Reichel, Uwe and Wierstorf, Hagen and Eyben, Florian and Schuller, Bj{\"orn

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1917--1924

Since several decades emotional databases have been recorded by various laboratories. Many of them contain acted portrays of Darwin`s famous {\textquotedblleft}big four{\textquotedblright} basic emotions. In this paper, we investigate in how far a selection of them are comparable by two approaches: on the one hand modeling similarity as performance in cross database machine learning experiments and on the other by analyzing a manually picked set of four acoustic features that represent different phonetic areas. It is interesting to see in how far specific databases (we added a synthetic one) perform well as a training set for others while some do not. Generally speaking, we found indications for both similarity as well as specificiality across languages.

null

24,611

inproceedings

burkhardt-etal-2022-nkululeko

Nkululeko: A Tool For Rapid Speaker Characteristics Detection

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.205/

Burkhardt, Felix and Wagner, Johannes and Wierstorf, Hagen and Eyben, Florian and Schuller, Bj{\"orn

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1925--1932

We present advancements with a software tool called Nkululeko, that lets users perform (semi-) supervised machine learning experiments in the speaker characteristics domain. It is based on audformat, a format for speech database metadata description. Due to an interface based on configurable templates, it supports best practise and very fast setup of experiments without the need to be proficient in the underlying language: Python. The paper explains the handling of Nkululeko and presents two typical experiments: comparing the expert acoustic features with artificial neural net embeddings for emotion classification and speaker age regression.

null

24,612

inproceedings

yu-etal-2022-speech

Speech Aerodynamics Database, Tools and Visualisation

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.206/

Yu, Shi and Ponchard, Clara and Trouville, Roland and Hassid, Sergio and Demolin, Didier

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1933--1938

Aerodynamic processes underlie the characteristics of the acoustic signal of speech sounds. The aerodynamics of speech give insights on acoustic outcome and help explain the mechanisms of speech production. This database was designed during an ARC project {\textquotedblright}Dynamique des syst{\`e}mes phonologiques{\textquotedblright} in which the study of aerodynamic constraints on speech production was an important target. Data were recorded between 1996 and 1999 at the Erasmus Hospital (H{\^o}pital Erasme) of Universit{\'e} Libre de Bruxelles, Belgium and constitute one of the few datasets available on direct measurement of subglottal pressure and other aerodynamic parameters. The goal was to obtain a substantial amount of data with simultaneous recording, in various context, of the speech acoustic signal, subglottal pressure (Ps), intraoral pressure (Po), oral airflow (Qo) and nasal airflow (Qn). This database contains recordings of 2 English, 1 Amharic, and 7 French speakers and is provided with data conversion and visualisation tools. Another aim of this project was to obtain some reference values of the aerodynamics of speech production for female and male speakers uttering different types of segments and sentences in French.

null

24,613

inproceedings

fougeron-etal-2022-patatra

{PATATRA} and {PATAF}req: two {F}rench databases for the documentation of within-speaker variability in speech

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.207/

Fougeron, C{\'e}cile and Audibert, Nicolas and Gendrot, Cedric and Chardenon, Estelle and Wohmann, Louise

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1939--1944

Our knowledge on speech is historically built on data comparing different speakers or data averaged across speakers. Consequently, little is known on the variability in the speech of a single individual. Experimental studies have shown that speakers adapt to the linguistic and the speaking contexts, and modify their speech according to their emotional or biological condition, etc. However, it is unclear how much speakers vary from one repetition to the next, and how comparable are recordings that are collected days, months or years apart. In this paper, we introduce two French databases which contain recordings of 9 to 11 speakers recorded over 9 to 18 sessions, allowing comparisons of speech tasks with a different delay between the repetitions: 3 repetitions within the same session, 6 to 10 repetitions on different days during a two months period, 5 to 9 repetitions on different years. Speakers are recorded on a large set of speech tasks including read and spontaneous speech as well as speech-like performance tasks. In this paper, we provide detailed descriptions of the two databases and available annotations. We conclude by an illustration on how these data can inform on within-speaker variability of speech.

null

24,614

inproceedings

mukiibi-etal-2022-makerere

The Makerere Radio Speech Corpus: A {L}uganda Radio Corpus for Automatic Speech Recognition

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.208/

Mukiibi, Jonathan and Katumba, Andrew and Nakatumba-Nabende, Joyce and Hussein, Ali and Meyer, Joshua

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1945--1954

Building a usable radio monitoring automatic speech recognition (ASR) system is a challenging task for under-resourced languages and yet this is paramount in societies where radio is the main medium of public communication and discussions. Initial efforts by the United Nations in Uganda have proved how understanding the perceptions of rural people who are excluded from social media is important in national planning. However, these efforts are being challenged by the absence of transcribed speech datasets. In this paper, The Makerere Artificial Intelligence research lab releases a Luganda radio speech corpus of 155 hours. To our knowledge, this is the first publicly available radio dataset in sub-Saharan Africa. The paper describes the development of the voice corpus and presents baseline Luganda ASR performance results using Coqui STT toolkit, an open-source speech recognition toolkit.

null

24,615

inproceedings

rouvier-mohammadamini-2022-far

Far-Field Speaker Recognition Benchmark Derived From The {D}i{PC}o Corpus

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.209/

Rouvier, Mickael and Mohammadamini, Mohammad

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1955--1959

In this paper, we present a far-field speaker verification benchmark derived from the publicly-available DiPCo corpus. This corpus comprise three different tasks that involve enrollment and test conditions with single- and/or multi-channels recordings. The main goal of this corpus is to foster research in far-field and multi-channel text-independent speaker verification. Also, it can be used for other speaker recognition tasks such as dereverberation, denoising and speech enhancement. In addition, we release a Kaldi and SpeechBrain system to facilitate further research. And we validate the evaluation design with a single-microphone state-of-the-art speaker recognition system (i.e. ResNet-101). The results show that the proposed tasks are very challenging. And we hope these resources will inspire the speech community to develop new methods and systems for this challenging domain.

null

24,616

inproceedings

wang-etal-2022-evaluating

Evaluating Sampling-based Filler Insertion with Spontaneous {TTS}

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.210/

Wang, Siyang and Gustafson, Joakim and Sz{\'e}kely, {\'E}va

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1960--1969

Inserting fillers (such as {\textquotedblleft}um{\textquotedblright}, {\textquotedblleft}like{\textquotedblright}) to clean speech text has a rich history of study. One major application is to make dialogue systems sound more spontaneous. The ambiguity of filler occurrence and inter-speaker difference make both modeling and evaluation difficult. In this paper, we study sampling-based filler insertion, a simple yet unexplored approach to inserting fillers. We propose an objective score called Filler Perplexity (FPP). We build three models trained on two single-speaker spontaneous corpora, and evaluate them with FPP and perceptual tests. We implement two innovations in perceptual tests, (1) evaluating filler insertion on dialogue systems output, (2) synthesizing speech with neural spontaneous TTS engines. FPP proves to be useful in analysis but does not correlate well with perceptual MOS. Perceptual results show little difference between compared filler insertion models including with ground-truth, which may be due to the ambiguity of what is good filler insertion and a strong neural spontaneous TTS that produces natural speech irrespective of input. Results also show preference for filler-inserted speech synthesized with spontaneous TTS. The same test using TTS based on read speech obtains the opposite results, which shows the importance of using spontaneous TTS in evaluating filler insertions. Audio samples: www.speech.kth.se/tts-demos/LREC22

null

24,617

inproceedings

mihajlik-etal-2022-bea

{BEA}-Base: A Benchmark for {ASR} of Spontaneous {H}ungarian

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.211/

Mihajlik, Peter and Balog, Andras and Graczi, Tekla Etelka and Kohari, Anna and Tarj{\'a}n, Bal{\'a}zs and Mady, Katalin

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1970--1977

Hungarian is spoken by 15 million people, still, easily accessible Automatic Speech Recognition (ASR) benchmark datasets {--} especially for spontaneous speech {--} have been practically unavailable. In this paper, we introduce BEA-Base, a subset of the BEA spoken Hungarian database comprising mostly spontaneous speech of 140 speakers. It is built specifically to assess ASR, primarily for conversational AI applications. After defining the speech recognition subsets and task, several baselines {--} including classic HMM-DNN hybrid and end-to-end approaches augmented by cross-language transfer learning {--} are developed using open-source toolkits. The best results obtained are based on multilingual self-supervised pretraining, achieving a 45{\%} recognition error rate reduction as compared to the classical approach {--} without the application of an external language model or additional supervised data. The results show the feasibility of using BEA-Base for training and evaluation of Hungarian speech recognition systems.

null

24,618

inproceedings

barker-etal-2022-snuc

{SN}u{C}: The {S}heffield Numbers Spoken Language Corpus

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.212/

Barker, Emma and Barker, Jon and Gaizauskas, Robert and Ma, Ning and Paramita, Monica Lestari

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1978--1984

We present SNuC, the first published corpus of spoken alphanumeric identifiers of the sort typically used as serial and part numbers in the manufacturing sector. The dataset contains recordings and transcriptions of over 50 native British English speakers, speaking over 13,000 multi-character alphanumeric sequences and totalling almost 20 hours of recorded speech. We describe requirements taken into account in the designing the corpus and the methodology used to construct it. We present summary statistics describing the corpus contents, as well as a preliminary investigation into errors in spoken alphanumeric identifiers. We validate the corpus by showing how it can be used to adapt a deep learning neural network based ASR system, resulting in improved recognition accuracy on the task of spoken alphanumeric identifier recognition. Finally, we discuss further potential uses for the corpus and for the tools developed to construct it.

null

24,619

inproceedings

zhao-chodroff-2022-mandi

The {M}an{D}i Corpus: A Spoken Corpus of {M}andarin Regional Dialects

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.213/

Zhao, Liang and Chodroff, Eleanor

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1985--1990

In the present paper, we introduce the ManDi Corpus, a spoken corpus of regional Mandarin dialects and Standard Mandarin. The corpus currently contains 357 recordings (about 9.6 hours) of monosyllabic words, disyllabic words, short sentences, a short passage and a poem, each produced in Standard Mandarin and in one of six regional Mandarin dialects: Beijing, Chengdu, Jinan, Taiyuan, Wuhan, and Xi`an Mandarin from 36 speakers. The corpus was collected remotely using participant-controlled smartphone recording apps. Word- and phone-level alignments were generated using Praat and the Montreal Forced Aligner. The pilot study of dialect-specific tone systems showed that with practicable design and decent recording quality, remotely collected speech data can be suitable for analysis of relative patterns in acoustic-phonetic realization. The corpus is available on OSF (\url{https://osf.io/fgv4w/}) for non-commercial use under a CC BY-NC 3.0 license.

null

24,620

inproceedings

carbone-etal-2022-speed

The Speed-Vel Project: a Corpus of Acoustic and Aerodynamic Data to Measure Droplets Emission During Speech Interaction

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.214/

Carbone, Francesca and Bouchet, Gilles and Ghio, Alain and Legou, Thierry and Andr{\'e}, Carine and Lalain, Muriel and Kadri, Sabrina and Petrone, Caterina and Procino, Federica and Giovanni, Antoine

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1991--1999

Conversations (normal speech) or professional interactions (e.g., projected speech in the classroom) have been identified as situations with increased risk of exposure to SARS-CoV-2 due to the high production of droplets in the exhaled air. However, it is still unclear to what extent speech properties influence droplets emission during everyday life conversations. Here, we report the experimental protocol of three experiments aiming at measuring the velocity and the direction of the airflow, the number and size of droplets spread during speech interactions in French. We consider different phonetic conditions, potentially leading to a modulation of speech droplets production, such as voice intensity (normal vs. loud voice), articulation manner of phonemes (type of consonants and vowels) and prosody (i.e., the melody of the speech). Findings from these experiments will allow future simulation studies to predict the transport, dispersion and evaporation of droplets emitted under different speech conditions.

null

24,621

inproceedings

aicher-etal-2022-towards-speech

Towards Speech-only Opinion-level Sentiment Analysis

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.215/

Aicher, Annalena and Gazizullina, Alisa and Gusev, Aleksei and Matveev, Yuri and Minker, Wolfgang

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2000--2006

The growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker`s sentiment from speech only. Mapping the latter on user preferences enables to adapt to the user and individualize the requested information while increasing user satisfaction. In this paper, we explore the integration of rank consistent ordinal regression into a speech-only sentiment prediction task performed by ResNet-like systems. Furthermore, we use speaker verification extractors trained on larger datasets as low-level feature extractors. An improvement of performance is shown by fusing sentiment and pre-extracted speaker embeddings reducing the speaker bias of sentiment predictions. Numerous experiments on Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) databases show that we beat the baselines of state-of-the-art unimodal approaches. Using speech as the only modality combined with optimizing an order-sensitive objective function gets significantly closer to the sentiment analysis results of state-of-the-art multimodal systems.

null

24,622

inproceedings

van-boven-etal-2022-intersection

At the Intersection of {NLP} and Sustainable Development: Exploring the Impact of Demographic-Aware Text Representations in Modeling Value on a Corpus of Interviews

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.216/

van Boven, Goya and Hirmer, Stephanie and Conforti, Costanza

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2007--2021

This research explores automated text classification using data from Low{--} and Middle{--}Income Countries (LMICs). In particular, we explore enhancing text representations with demographic information of speakers in a privacy-preserving manner. We introduce the Demographic-Rich Qualitative UPV-Interviews Dataset (DR-QI), a rich dataset of qualitative interviews from rural communities in India and Uganda. The interviews were conducted following the latest standards for respectful interactions with illiterate speakers (Hirmer et al., 2021a). The interviews were later sentence-annotated for Automated User-Perceived Value (UPV) Classification (Conforti et al., 2020), a schema that classifies values expressed by speakers, resulting in a dataset of 5,333 sentences. We perform the UPV classification task, which consists of predicting which values are expressed in a given sentence, on the new DR-QI dataset. We implement a classification model using DistilBERT (Sanh et al., 2019), which we extend with demographic information. In order to preserve the privacy of speakers, we investigate encoding demographic information using autoencoders. We find that adding demographic information improves performance, even if such information is encoded. In addition, we find that the performance per UPV is linked to the number of occurrences of that value in our data.

null

24,623

inproceedings

gref-etal-2022-study

A Study on the Ambiguity in Human Annotation of {G}erman Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.217/

Gref, Michael and Matthiesen, Nike and Hikkal Venugopala, Sreenivasa and Satheesh, Shalaka and Vijayananth, Aswinkumar and Ha, Duc Bach and Behnke, Sven and K{\"ohler, Joachim

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2022--2031

For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes very subtle facets of emotions. Therefore, the question arises of the chance machines and humans have capturing and assigning these into predefined categories. This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems. Our experiments reveal substantial differences in human perception for different emotions. Furthermore, we report from ongoing machine learning experiments with different modalities. We show that the human perceptual ambiguity and other challenges, such as class imbalance and lack of training data, currently limit the opportunities of these technologies for oral history archives. Nonetheless, our work uncovers promising observations and possibilities for further research.

null

24,624

inproceedings

cobeli-etal-2022-detecting

Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.218/

Cobeli, Ștefan and Iordache, Ioan-Bogdan and Yadav, Shweta and Caragea, Cornelia and Dinu, Liviu P. and Iliescu, Dragoș

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2032--2041

Finding the polarity of feelings in texts is a far-reaching task. Whilst the field of natural language processing has established sentiment analysis as an alluring problem, many feelings are left uncharted. In this study, we analyze the optimism and pessimism concepts from Twitter posts to effectively understand the broader dimension of psychological phenomenon. Towards this, we carried a systematic study by first exploring the linguistic peculiarities of optimism and pessimism in user-generated content. Later, we devised a multi-task knowledge distillation framework to simultaneously learn the target task of optimism detection with the help of the auxiliary task of sentiment analysis and hate speech detection. We evaluated the performance of our proposed approach on the benchmark Optimism/Pessimism Twitter dataset. Our extensive experiments show the superior- ity of our approach in correctly differentiating between optimistic and pessimistic users. Our human and automatic evaluation shows that sentiment analysis and hate speech detection are beneficial for optimism/pessimism detection.

null

24,625

inproceedings

herath-etal-2022-dataset

Dataset and Baseline for Automatic Student Feedback Analysis

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.219/

Herath, Missaka and Chamindu, Kushan and Maduwantha, Hashan and Ranathunga, Surangika

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2042--2049

In this paper, we present a student feedback corpus, which contains 3000 instances of feedback written by university students. This dataset has been annotated for aspect terms, opinion terms, polarities of the opinion terms towards targeted aspects, document-level opinion polarities and sentence separations. We develop a hierarchical taxonomy for aspect categorization, which covers all the areas of the teaching-learning process. We annotated both implicit and explicit aspects using this taxonomy. Annotation methodology, difficulties faced during the annotation, and the details about the aspect term categorization have been discussed in detail. This annotated corpus can be used for Aspect Extraction, Aspect Level Sentiment Analysis, and Document Level Sentiment Analysis. Also the baseline results for all three tasks are given in the paper.

null

24,626

inproceedings

tikhonov-etal-2022-eenlp

{EENLP}: Cross-lingual {E}astern {E}uropean {NLP} Index

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.220/

Tikhonov, Alexey and Malkhasov, Alex and Manoshin, Andrey and Dima, George-Andrei and Cserh{\'a}ti, R{\'e}ka and Hossain Asif, Md.Sadek and S{\'a}rdi, Matt

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2050--2057

Motivated by the sparsity of NLP resources for Eastern European languages, we present a broad index of existing Eastern European language resources (90+ datasets and 45+ models) published as a github repository open for updates from the community. Furthermore, to support the evaluation of commonsense reasoning tasks, we provide hand-crafted cross-lingual datasets for five different semantic tasks (namely news categorization, paraphrase detection, Natural Language Inference (NLI) task, tweet sentiment detection, and news sentiment detection) for some of the Eastern European languages. We perform several experiments with the existing multilingual models on these datasets to define the performance baselines and compare them to the existing results for other languages.

null

24,627

inproceedings

zagar-robnik-sikonja-2022-slovene

{S}lovene {S}uper{GLUE} Benchmark: Translation and Evaluation

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.221/

{\v{Z}}agar, Ale{\v{s}} and Robnik-{\v{S}}ikonja, Marko

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2058--2065

We present SuperGLUE benchmark adapted and translated into Slovene using a combination of human and machine translation. We describe the translation process and problems arising due to differences in morphology and grammar. We evaluate the translated datasets in several modes: monolingual, cross-lingual, and multilingual, taking into account differences between machine and human translated training sets. The results show that the monolingual Slovene SloBERTa model is superior to massively multilingual and trilingual BERT models, but these also show a good cross-lingual performance on certain tasks. The performance of Slovene models still lags behind the best English models.

null

24,628

inproceedings

zanon-boito-etal-2022-speech

Speech Resources in the {T}amasheq Language

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.222/

Zanon Boito, Marcely and Bougares, Fethi and Barbier, Florentin and Gahbiche, Souhir and Barrault, Lo{\"ic and Rouvier, Mickael and Est{\`eve, Yannick

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2066--2071

In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. These two datasets were made available for the IWSLT 2022 low-resource speech translation track, and they consist of collections of radio recordings from daily broadcast news in Niger (Studio Kalangou) and Mali (Studio Tamani). We share (i) a massive amount of unlabeled audio data (671 hours) in five languages: French from Niger, Fulfulde, Hausa, Tamasheq and Zarma, and (ii) a smaller 17 hours parallel corpus of audio recordings in Tamasheq, with utterance-level translations in the French language. All this data is shared under the Creative Commons BY-NC-ND 3.0 license. We hope these resources will inspire the speech community to develop and benchmark models using the Tamasheq language.

null

24,629

inproceedings

knyazeva-etal-2022-aesops

{A}esop`s fable {\textquotedblleft}The North Wind and the Sun{\textquotedblright} Used as a Rosetta Stone to Extract and Map Spoken Words in Under-resourced Languages

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.223/

Knyazeva, Elena and Boula de Mare{\"uil, Philippe and Vernier, Fr{\'ed{\'eric

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2072--2079

This paper describes a method of semi-automatic word spotting in minority languages, from one and the same Aesop fable {\textquotedblleft}The North Wind and the Sun{\textquotedblright} translated in Romance languages/dialects from Hexagonal (i.e. Metropolitan) France and languages from French Polynesia. The first task consisted of finding out how a dozen words such as {\textquotedblleft}wind{\textquotedblright} and {\textquotedblleft}sun{\textquotedblright} were translated in over 200 versions collected in the field {---} taking advantage of orthographic similarity, word position and context. Occurrences of the translations were then extracted from the phone-aligned recordings. The results were judged accurate in 96{--}97{\%} of cases, both on the development corpus and a test set of unseen data. Corrected alignments were then mapped and basemaps were drawn to make various linguistic phenomena immediately visible. The paper exemplifies how regular expressions may be used for this purpose. The final result, which takes the form of an online speaking atlas (enriching the \url{https://atlas.limsi.fr} website), enables us to illustrate lexical, morphological or phonetic variation.

null

24,630

inproceedings

palen-michel-etal-2022-multilingual

Multilingual Open Text Release 1: Public Domain News in 44 Languages

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.224/

Palen-Michel, Chester and Kim, June and Lignos, Constantine

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2080--2089

We present a Multilingual Open Text (MOT), a new multilingual corpus containing text in 44 languages, many of which have limited existing text resources for natural language processing. The first release of the corpus contains over 2.8 million news articles and an additional 1 million short snippets (photo captions, video descriptions, etc.) published between 2001{--}2022 and collected from Voice of America`s news websites. We describe our process for collecting, filtering, and processing the data. The source material is in the public domain, our collection is licensed using a creative commons license (CC BY 4.0), and all software used to create the corpus is released under the MIT License. The corpus will be regularly updated as additional documents are published.

null

24,631

inproceedings

herrera-etal-2022-tweettaglish

{T}weet{T}aglish: A Dataset for Investigating {T}agalog-{E}nglish Code-Switching

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.225/

Herrera, Megan and Aich, Ankit and Parde, Natalie

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2090--2097

Deploying recent natural language processing innovations to low-resource settings allows for state-of-the-art research findings and applications to be accessed across cultural and linguistic borders. One low-resource setting of increasing interest is code-switching, the phenomenon of combining, swapping, or alternating the use of two or more languages in continuous dialogue. In this paper, we introduce a large dataset (20k+ instances) to facilitate investigation of Tagalog-English code-switching, which has become a popular mode of discourse in Philippine culture. Tagalog is an Austronesian language and former official language of the Philippines spoken by over 23 million people worldwide, but it and Tagalog-English are under-represented in NLP research and practice. We describe our methods for data collection, as well as our labeling procedures. We analyze our resulting dataset, and finally conclude by providing results from a proof-of-concept regression task to establish dataset validity, achieving a strong performance benchmark (R2=0.797-0.909; RMSE=0.068-0.057).

null

24,632

inproceedings

chiruzzo-etal-2022-jojajovai

Jojajovai: A Parallel {G}uarani-{S}panish Corpus for {MT} Benchmarking

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.226/

Chiruzzo, Luis and G{\'ongora, Santiago and Alvarez, Aldo and Gim{\'enez-Lugo, Gustavo and Ag{\"uero-Torales, Marvin and Rodr{\'iguez, Yliana

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2098--2107

This work presents a parallel corpus of Guarani-Spanish text aligned at sentence level. The corpus contains about 30,000 sentence pairs, and is structured as a collection of subsets from different sources, further split into training, development and test sets. A sample of sentences from the test set was manually annotated by native speakers in order to incorporate meta-linguistic annotations about the Guarani dialects present in the corpus and also the correctness of the alignment and translation. We also present some baseline MT experiments and analyze the results in terms of the subsets. We hope this corpus can be used as a benchmark for testing Guarani-Spanish MT systems, and aim to expand and improve the quality of the corpus in future iterations.

null

24,633

inproceedings

viksna-etal-2022-assessing

Assessing Multilinguality of Publicly Accessible Websites

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.227/

V{\={i}}ksna, Rinalds and Skadi{\c{n}}a, Inguna and Skadi{\c{n}}{\v{s}}, Raivis and Vasi{\c{l}}jevs, Andrejs and Rozis, Roberts

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2108--2116

Although information on the Internet can be shared in many languages, the language presence on the World Wide Web is very disproportionate. The problem of multilingualism on the Web, in particular access, availability and quality of information in the world`s languages, has been the subject of UNESCO focus for several decades. Making European websites more multilingual is also one of the focal targets of the Connecting Europe Facility Automated Translation (CEF AT) digital service infrastructure. In order to monitor this goal, alongside other possible solutions, CEF AT needs a methodology and easy to use tool to assess the degree of multilingualism of a given website. In this paper we investigate methods and tools that automatically analyse the language diversity of the Web and propose indicators and methodology on how to measure the multilingualism of European websites. We also introduce a prototype tool based on open-source software that helps to assess multilingualism of the Web and can be independently run at set intervals. We also present initial results obtained with our tool that allows us to conclude that multilingualism on the Web is still a problem not only at the world level, but also at the European and regional level.

null

24,634

inproceedings

kletz-etal-2022-methodology

A Methodology for Building a Diachronic Dataset of Semantic Shifts and its Application to {QC}-{FR}-Diac-V1.0, a Free Reference for {F}rench

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.228/

Kletz, David and Langlais, Philippe and Lareau, Fran{\c{c}}ois and Drouin, Patrick

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2117--2125

Different algorithms have been proposed to detect semantic shifts (changes in a word meaning over time) in a diachronic corpus. Yet, and somehow surprisingly, no reference corpus has been designed so far to evaluate them, leaving researchers to fallback to troublesome evaluation strategies. In this work, we introduce a methodology for the construction of a reference dataset for the evaluation of semantic shift detection, that is, a list of words where we know for sure whether they present a word meaning change over a period of interest. We leverage a state-of-the-art word-sense disambiguation model to associate a date of first appearance to all the senses of a word. Significant changes in sense distributions as well as clear stability are detected and the resulting words are inspected by experts using a dedicated interface before populating a reference dataset. As a proof of concept, we apply this methodology to a corpus of newspapers from Quebec covering the whole 20th century. We manually verified a subset of candidates, leading to QC-FR-Diac-V1.0, a corpus of 151 words allowing one to evaluate the identification of semantic shifts in French between 1910 and 1990.

null

24,635

inproceedings

frohberg-binder-2022-crass

{CRASS}: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Models

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.229/

Frohberg, J{\"org and Binder, Frank

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2126--2140

We introduce the CRASS (counterfactual reasoning assessment) data set and benchmark utilizing questionized counterfactual conditionals as a novel and powerful tool to evaluate large language models. We present the data set design and benchmark. We test six state-of-the-art models against our benchmark. Our results show that it poses a valid challenge for these models and opens up considerable room for their improvement.

null

24,636

inproceedings

costa-jussa-etal-2022-evaluating

Evaluating Gender Bias in Speech Translation

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.230/

Costa-juss{\`a}, Marta R. and Basta, Christine and G{\'a}llego, Gerard I.

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2141--2147

The scientific community is increasingly aware of the necessity to embrace pluralism and consistently represent major and minor social groups. Currently, there are no standard evaluation techniques for different types of biases. Accordingly, there is an urgent need to provide evaluation sets and protocols to measure existing biases in our automatic systems. Evaluating the biases should be an essential step towards mitigating them in the systems. This paper introduces WinoST, a new freely available challenge set for evaluating gender bias in speech translation. WinoST is the speech version of WinoMT, an MT challenge set, and both follow an evaluation protocol to measure gender accuracy. Using an S-Transformer end-to-end speech translation system, we report the gender bias evaluation on four language pairs, and we reveal the inaccuracies in translations generating gender-stereotyped translations.

null

24,637

inproceedings

scholman-etal-2022-design

Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.231/

Scholman, Merel and Pyatkin, Valentina and Yung, Frances and Dagan, Ido and Tsarfaty, Reut and Demberg, Vera

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2148--2156

Obtaining linguistic annotation from novice crowdworkers is far from trivial. A case in point is the annotation of discourse relations, which is a complicated task. Recent methods have obtained promising results by extracting relation labels from either discourse connectives (DCs) or question-answer (QA) pairs that participants provide. The current contribution studies the effect of worker selection and training on the agreement on implicit relation labels between workers and gold labels, for both the DC and the QA method. In Study 1, workers were not specifically selected or trained, and the results show that there is much room for improvement. Study 2 shows that a combination of selection and training does lead to improved results, but the method is cost- and time-intensive. Study 3 shows that a selection-only approach is a viable alternative; it results in annotations of comparable quality compared to annotations from trained participants. The results generalized over both the DC and QA method and therefore indicate that a selection-only approach could also be effective for other crowdsourced discourse annotation tasks.

null

24,638

inproceedings

kulkarni-etal-2022-tbd3

{TBD}3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.232/

Kulkarni, Hrishikesh and MacAvaney, Sean and Goharian, Nazli and Frieder, Ophir

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2157--2165

Social media are heavily used by many users to share their mental health concerns and diagnoses. This trend has turned social media into a large-scale resource for researchers focused on detecting mental health conditions. Social media usage varies considerably across individuals. Thus, classification of patterns, including detecting signs of depression, must account for such variation. We address the disparity in classification effectiveness for users with little activity (e.g., new users). Our evaluation, performed on a large-scale dataset, shows considerable detection discrepancy based on user posting frequency. For instance, the F1 detection score of users with an above-median versus below-median number of posts is greater than double (0.803 vs 0.365) using a conventional CNN-based model; similar results were observed on lexical and transformer-based classifiers. To complement this evaluation, we propose a dynamic thresholding technique that adjusts the classifier`s sensitivity as a function of the number of posts a user has. This technique alone reduces the margin between users with many and few posts, on average, by 45{\%} across all methods and increases overall performance, on average, by 33{\%}. These findings emphasize the importance of evaluating and tuning natural language systems for potentially vulnerable populations.

null

24,639

inproceedings

ghosh-etal-2022-specnfs

{S}pec{NFS}: A Challenge Dataset Towards Extracting Formal Models from Natural Language Specifications

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.233/

Ghosh, Sayontan and Singh, Amanpreet and Merenstein, Alex and Su, Wei and Smolka, Scott A. and Zadok, Erez and Balasubramanian, Niranjan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2166--2176

Can NLP assist in building formal models for verifying complex systems? We study this challenge in the context of parsing Network File System (NFS) specifications. We define a semantic-dependency problem over SpecIR, a representation language we introduce to model sentences appearing in NFS specification documents (RFCs) as IF-THEN statements, and present an annotated dataset of 1,198 sentences. We develop and evaluate semantic-dependency parsing systems for this problem. Evaluations show that even when using a state-of-the-art language model, there is significant room for improvement, with the best models achieving an F1 score of only 60.5 and 33.3 in the named-entity-recognition and dependency-link-prediction sub-tasks, respectively. We also release additional unlabeled data and other domain-related texts. Experiments show that these additional resources increase the F1 measure when used for simple domain-adaption and transfer-learning-based approaches, suggesting fruitful directions for further research

null

24,640

inproceedings

bai-stede-2022-argument

Argument Similarity Assessment in {G}erman for Intelligent Tutoring: Crowdsourced Dataset and First Experiments

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.234/

Bai, Xiaoyu and Stede, Manfred

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2177--2187

NLP technologies such as text similarity assessment, question answering and text classification are increasingly being used to develop intelligent educational applications. The long-term goal of our work is an intelligent tutoring system for German secondary schools, which will support students in a school exercise that requires them to identify arguments in an argumentative source text. The present paper presents our work on a central subtask, viz. the automatic assessment of similarity between a pair of argumentative text snippets in German. In the designated use case, students write out key arguments from a given source text; the tutoring system then evaluates them against a target reference, assessing the similarity level between student work and the reference. We collect a dataset for our similarity assessment task through crowdsourcing as authentic German student data are scarce; we label the collected text pairs with similarity scores on a 5-point scale and run first experiments on the task. We see that a model based on BERT shows promising results, while we also discuss some challenges that we observe.

null

24,641

inproceedings

jain-etal-2022-leveraging

Leveraging Pre-trained Language Models for Gender Debiasing

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.235/

Jain, Nishtha and Groves, Declan and Specia, Lucia and Popovi{\'c}, Maja

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2188--2195

Studying and mitigating gender and other biases in natural language have become important areas of research from both algorithmic and data perspectives. This paper explores the idea of reducing gender bias in a language generation context by generating gender variants of sentences. Previous work in this field has either been rule-based or required large amounts of gender balanced training data. These approaches are however not scalable across multiple languages, as creating data or rules for each language is costly and time-consuming. This work explores a light-weight method to generate gender variants for a given text using pre-trained language models as the resource, without any task-specific labelled data. The approach is designed to work on multiple languages with minimal changes in the form of heuristics. To showcase that, we have tested it on a high-resourced language, namely Spanish, and a low-resourced language from a different family, namely Serbian. The approach proved to work very well on Spanish, and while the results were less positive for Serbian, it showed potential even for languages where pre-trained models are less effective.

null

24,642

inproceedings

de-la-pena-sarracen-rosso-2022-unsupervised

Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.236/

De la Pe{\~n}a Sarrac{\'e}n, Gretel Liz and Rosso, Paolo

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2196--2204

Hate speech detection is a prominent and challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author. Hence, many models suffer from the generalization problem. However, retrieving and monitoring hateful content on social media is a current necessity. In this paper, we propose an unsupervised approach using Graph Auto-Encoders (GAE), which allows us to avoid using labeled data when training the representation of the texts. Specifically, we represent texts as nodes of a graph, and use a transformer layer together with a convolutional layer to encode these nodes in a low-dimensional space. As a result, we obtain embeddings that can be decoded into a reconstruction of the original network. Our main idea is to learn a model with a set of texts without supervision, in order to generate embeddings for the nodes: nodes with the same label should be close in the embedding space, which, in turn, should allow us to distinguish among classes. We employ this strategy to detect hate speech in multi-domain and multilingual sets of texts, where our method shows competitive results on small datasets.

null

24,643

inproceedings

heinrich-etal-2022-fquad2

{FQ}u{AD}2.0: {F}rench Question Answering and Learning When You Don`t Know

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.237/

Heinrich, Quentin and Viaud, Gautier and Belblidia, Wacim

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2205--2214

Question Answering, including Reading Comprehension, is one of the NLP research areas that has seen significant scientific breakthroughs over the past few years, thanks to the concomitant advances in Language Modeling. Most of these breakthroughs, however, are centered on the English language. In 2020, as a first strong initiative to bridge the gap to the French language, Illuin Technology introduced FQuAD1.1, a French Native Reading Comprehension dataset composed of 60,000+ questions and answers samples extracted from Wikipedia articles. Nonetheless, Question Answering models trained on this dataset have a major drawback: they are not able to predict when a given question has no answer in the paragraph of interest, therefore making unreliable predictions in various industrial use-cases. We introduce FQuAD2.0, which extends FQuAD with 17,000+ unanswerable questions, annotated adversarially, in order to be similar to answerable ones. This new dataset, comprising a total of almost 80,000 questions, makes it possible to train French Question Answering models with the ability of distinguishing unanswerable questions from answerable ones. We benchmark several models with this dataset: our best model, a fine-tuned CamemBERT-large, achieves a F1 score of 82.3{\%} on this classification task, and a F1 score of 83{\%} on the Reading Comprehension task.

null

24,644

inproceedings

toraman-etal-2022-large

Large-Scale Hate Speech Detection with Cross-Domain Transfer

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.238/

Toraman, Cagri and {\c{S}}ahinu{\c{c}}, Furkan and Yilmaz, Eyup

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2215--2225

The performance of hate speech detection models relies on the datasets on which the models are trained. Existing datasets are mostly prepared with a limited number of instances or hate domains that define hate topics. This hinders large-scale analysis and transfer learning with respect to hate domains. In this study, we construct large-scale tweet datasets for hate speech detection in English and a low-resource language, Turkish, consisting of human-labeled 100k tweets per each. Our datasets are designed to have equal number of tweets distributed over five domains. The experimental results supported by statistical tests show that Transformer-based language models outperform conventional bag-of-words and neural models by at least 5{\%} in English and 10{\%} in Turkish for large-scale hate speech detection. The performance is also scalable to different training sizes, such that 98{\%} of performance in English, and 97{\%} in Turkish, are recovered when 20{\%} of training instances are used. We further examine the generalization ability of cross-domain transfer among hate domains. We show that 96{\%} of the performance of a target domain in average is recovered by other domains for English, and 92{\%} for Turkish. Gender and religion are more successful to generalize to other domains, while sports fail most.

null

24,645

inproceedings

meyer-elsweiler-2022-glohbcd

{GL}o{HBCD}: A Naturalistic {G}erman Dataset for Language of Health Behaviour Change on Online Support Forums

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.239/

Meyer, Selina and Elsweiler, David

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2226--2235

Health behaviour change is a difficult and prolonged process that requires sustained motivation and determination. Conversa- tional agents have shown promise in supporting the change process in the past. One therapy approach that facilitates change and has been used as a framework for conversational agents is motivational interviewing. However, existing implementations of this therapy approach lack the deep understanding of user utterances that is essential to the spirit of motivational interviewing. To address this lack of understanding, we introduce the GLoHBCD, a German dataset of naturalistic language around health behaviour change. Data was sourced from a popular German weight loss forum and annotated using theoretically grounded motivational interviewing categories. We describe the process of dataset construction and present evaluation results. Initial experiments suggest a potential for broad applicability of the data and the resulting classifiers across different behaviour change domains. We make code to replicate the dataset and experiments available on Github.

null

24,646

inproceedings

hendrickx-2022-creating

Creating a Data Set of Abstractive Summaries of Turn-labeled Spoken Human-Computer Conversations

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.240/

Hendrickx, Iris

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2236--2244

Digital recorded written and spoken dialogues are becoming increasingly available as an effect of the technological advances such as online messenger services and the use of chatbots. Summaries are a natural way of presenting the important information gathered from dialogues. We present a unique data set that consists of Dutch spoken human-computer conversations, an annotation layer of turn labels, and conversational abstractive summaries of user answers. The data set is publicly available for research purposes.

null

24,647

inproceedings

cui-etal-2022-openel

{O}pen{EL}: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.241/

Cui, Wen and Rolston, Leanne and Walker, Marilyn and Hockey, Beth Ann

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2245--2256

Entity linking in dialogue is the task of mapping entity mentions in utterances to a target knowledge base. Prior work on entity linking has mainly focused on well-written articles such as Wikipedia, annotated newswire, or domain-specific datasets. We extend the study of entity linking to open domain dialogue by presenting the OpenEL corpus: an annotated multi-domain corpus for linking entities in natural conversation to Wikidata. Each dialogic utterance in 179 dialogues over 12 topics from the EDINA dataset has been annotated for entities realized by definite referring expressions as well as anaphoric forms such as he, she, it and they. This dataset supports training and evaluation of entity linking in open-domain dialogue, as well as analysis of the effect of using dialogue context and anaphora resolution in model training. It could also be used for fine-tuning a coreference resolution algorithm. To the best of our knowledge, this is the first substantial entity linking corpus publicly available for open-domain dialogue. We also establish baselines for this task using several existing entity linking systems. We found that the Transformer-based system Flair + BLINK has the best performance with a 0.65 F1 score. Our results show that dialogue context is extremely beneficial for entity linking in conversations, with Flair + Blink achieving an F1 of 0.61 without discourse context. These results also demonstrate the remaining performance gap between the baselines and human performance, highlighting the challenges of entity linking in open-domain dialogue, and suggesting many avenues for future research using OpenEL.

null

24,648

inproceedings

willemsen-etal-2022-collecting

Collecting Visually-Grounded Dialogue with A Game Of Sorts

Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.lrec-1.242/

Willemsen, Bram and Kalpakchi, Dmytro and Skantze, Gabriel

Proceedings of the Thirteenth Language Resources and Evaluation Conference

2257--2268

An idealized, though simplistic, view of the referring expression production and grounding process in (situated) dialogue assumes that a speaker must merely appropriately specify their expression so that the target referent may be successfully identified by the addressee. However, referring in conversation is a collaborative process that cannot be aptly characterized as an exchange of minimally-specified referring expressions. Concerns have been raised regarding assumptions made by prior work on visually-grounded dialogue that reveal an oversimplified view of conversation and the referential process. We address these concerns by introducing a collaborative image ranking task, a grounded agreement game we call {\textquotedblleft}A Game Of Sorts{\textquotedblright}. In our game, players are tasked with reaching agreement on how to rank a set of images given some sorting criterion through a largely unrestricted, role-symmetric dialogue. By putting emphasis on the argumentation in this mixed-initiative interaction, we collect discussions that involve the collaborative referential process. We describe results of a small-scale data collection experiment with the proposed task. All discussed materials, which includes the collected data, the codebase, and a containerized version of the application, are publicly available.

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24,649