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

inproceedings

lamproudis-etal-2022-evaluating

Evaluating Pretraining Strategies for Clinical {BERT} 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-1.43/

Lamproudis, Anastasios and Henriksson, Aron and Dalianis, Hercules

Proceedings of the Thirteenth Language Resources and Evaluation Conference

410--416

Research suggests that using generic language models in specialized domains may be sub-optimal due to significant domain differences. As a result, various strategies for developing domain-specific language models have been proposed, including techniques for adapting an existing generic language model to the target domain, e.g. through various forms of vocabulary modifications and continued domain-adaptive pretraining with in-domain data. Here, an empirical investigation is carried out in which various strategies for adapting a generic language model to the clinical domain are compared to pretraining a pure clinical language model. Three clinical language models for Swedish, pretrained for up to ten epochs, are fine-tuned and evaluated on several downstream tasks in the clinical domain. A comparison of the language models' downstream performance over the training epochs is conducted. The results show that the domain-specific language models outperform a general-domain language model; however, there is little difference in performance of the various clinical language models. However, compared to pretraining a pure clinical language model with only in-domain data, leveraging and adapting an existing general-domain language model requires fewer epochs of pretraining with in-domain data.

null

24,450

inproceedings

yeshpanov-etal-2022-kaznerd

{K}az{NERD}: {K}azakh Named Entity Recognition 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-1.44/

Yeshpanov, Rustem and Khassanov, Yerbolat and Varol, Huseyin Atakan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

417--426

We present the development of a dataset for Kazakh named entity recognition. The dataset was built as there is a clear need for publicly available annotated corpora in Kazakh, as well as annotation guidelines containing straightforward{---}but rigorous{---}rules and examples. The dataset annotation, based on the IOB2 scheme, was carried out on television news text by two native Kazakh speakers under the supervision of the first author. The resulting dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. State-of-the-art machine learning models to automatise Kazakh named entity recognition were also built, with the best-performing model achieving an exact match F1-score of 97.22{\%} on the test set. The annotated dataset, guidelines, and codes used to train the models are freely available for download under the CC BY 4.0 licence from \url{https://github.com/IS2AI/KazNERD}.

null

24,451

inproceedings

mersinias-valvis-2022-mitigating

Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization

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-1.45/

Mersinias, Michail and Valvis, Panagiotis

Proceedings of the Thirteenth Language Resources and Evaluation Conference

427--435

In recent years, natural language inference has been an emerging research area. In this paper, we present a novel data augmentation technique and combine it with a unique learning procedure for that task. Our so-called automatic contextual data augmentation (acda) method manages to be fully automatic, non-trivially contextual, and computationally efficient at the same time. When compared to established data augmentation methods, it is substantially more computationally efficient and requires no manual annotation by a human expert as they usually do. In order to increase its efficiency, we combine acda with two learning optimization techniques: contrastive learning and a hybrid loss function. The former maximizes the benefit of the supervisory signal generated by acda, while the latter incentivises the model to learn the nuances of the decision boundary. Our combined approach is shown experimentally to provide an effective way for mitigating spurious data correlations within a dataset, called dataset artifacts, and as a result improves performance. Specifically, our experiments verify that acda-boosted pre-trained language models that employ our learning optimization techniques, consistently outperform the respective fine-tuned baseline pre-trained language models across both benchmark datasets and adversarial examples.

null

24,452

inproceedings

zhang-etal-2022-kompetencer

Kompetencer: Fine-grained Skill Classification in {D}anish Job Postings via Distant Supervision and Transfer 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-1.46/

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

Proceedings of the Thirteenth Language Resources and Evaluation Conference

436--447

Skill Classification (SC) is the task of classifying job competences from job postings. This work is the first in SC applied to Danish job vacancy data. We release the first Danish job posting dataset: *Kompetencer* ({\_}en{\_}: competences), annotated for nested spans of competences. To improve upon coarse-grained annotations, we make use of The European Skills, Competences, Qualifications and Occupations (ESCO; le Vrang et al., (2014)) taxonomy API to obtain fine-grained labels via distant supervision. We study two setups: The zero-shot and few-shot classification setting. We fine-tune English-based models and RemBERT (Chung et al., 2020) and compare them to in-language Danish models. Our results show RemBERT significantly outperforms all other models in both the zero-shot and the few-shot setting.

null

24,453

inproceedings

bakker-etal-2022-semantic

Semantic Role Labelling for {D}utch Law 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-1.47/

Bakker, Roos and van Drie, Romy A.N. and de Boer, Maaike and van Doesburg, Robert and van Engers, Tom

Proceedings of the Thirteenth Language Resources and Evaluation Conference

448--457

Legal texts are often difficult to interpret, and people who interpret them need to make choices about the interpretation. To improve transparency, the interpretation of a legal text can be made explicit by formalising it. However, creating formalised representations of legal texts manually is quite labour-intensive. In this paper, we describe a method to extract structured representations in the Flint language (van Doesburg and van Engers, 2019) from natural language. Automated extraction of knowledge representation not only makes the interpretation and modelling efforts more efficient, it also contributes to reducing inter-coder dependencies. The Flint language offers a formal model that enables the interpretation of legal text by describing the norms in these texts as acts, facts and duties. To extract the components of a Flint representation, we use a rule-based method and a transformer-based method. In the transformer-based method we fine-tune the last layer with annotated legal texts. The results show that the transformed-based method (80{\%} accuracy) outperforms the rule-based method (42{\%} accuracy) on the Dutch Aliens Act. This indicates that the transformer-based method is a promising approach of automatically extracting Flint frames.

null

24,454

inproceedings

goslin-hofmann-2022-english

{E}nglish Language Spelling Correction as an Information Retrieval Task Using {W}ikipedia Search Statistics

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-1.48/

Goslin, Kyle and Hofmann, Markus

Proceedings of the Thirteenth Language Resources and Evaluation Conference

458--464

Spelling correction utilities have become commonplace during the writing process, however, many spelling correction utilities suffer due to the size and quality of dictionaries available to aid correction. Many terms, acronyms, and morphological variations of terms are often missing, leaving potential spelling errors unidentified and potentially uncorrected. This research describes the implementation of WikiSpell, a dynamic spelling correction tool that relies on the Wikipedia dataset search API functionality as the sole source of knowledge to aid misspelled term identification and automatic replacement. Instead of a traditional matching process to select candidate replacement terms, the replacement process is treated as a natural language information retrieval process harnessing wildcard string matching and search result statistics. The aims of this research include: 1) the implementation of a spelling correction algorithm that utilizes the wildcard operators in the Wikipedia dataset search API, 2) a review of the current spell correction tools and approaches being utilized, and 3) testing and validation of the developed algorithm against the benchmark spelling correction tool, Hunspell. The key contribution of this research is a robust, dynamic information retrieval-based spelling correction algorithm that does not require prior training. Results of this research show that the proposed spelling correction algorithm, WikiSpell, achieved comparable results to an industry-standard spelling correction algorithm, Hunspell.

null

24,455

inproceedings

lee-etal-2022-crudeoilnews

{C}rude{O}il{N}ews: An Annotated Crude Oil News Corpus for Event Extraction

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-1.49/

Lee, Meisin and Soon, Lay-Ki and Siew, Eu Gene and Sugianto, Ly Fie

Proceedings of the Thirteenth Language Resources and Evaluation Conference

465--479

In this paper, we present CrudeOilNews, a corpus of English Crude Oil news for event extraction. It is the first of its kind for Commodity News and serves to contribute towards resource building for economic and financial text mining. This paper describes the data collection process, the annotation methodology, and the event typology used in producing the corpus. Firstly, a seed set of 175 news articles were manually annotated, of which a subset of 25 news was used as the adjudicated reference test set for inter-annotator and system evaluation. The inter-annotator agreement was generally substantial, and annotator performance was adequate, indicating that the annotation scheme produces consistent event annotations of high quality. Subsequently, the dataset is expanded through (1) data augmentation and (2) Human-in-the-loop active learning. The resulting corpus has 425 news articles with approximately 11k events annotated. As part of the active learning process, the corpus was used to train basic event extraction models for machine labeling; the resulting models also serve as a validation or as a pilot study demonstrating the use of the corpus in machine learning purposes. The annotated corpus is made available for academic research purpose at \url{https://github.com/meisin/CrudeOilNews-Corpus}

null

24,456

inproceedings

dehio-etal-2022-claim

Claim Extraction and Law Matching for {COVID}-19-related Legislation

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-1.50/

Dehio, Niklas and Ostendorff, Malte and Rehm, Georg

Proceedings of the Thirteenth Language Resources and Evaluation Conference

480--490

To cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what is currently allowed or forbidden since news articles typically do not reference underlying laws. We investigate an automated approach to extract legal claims from news articles and to match the claims with their corresponding applicable laws. We examine the feasibility of the two tasks concerning claims about COVID-19-related laws from Berlin, Germany. For both tasks, we create and make publicly available the data sets and report the results of initial experiments. We obtain promising results with Transformer-based models that achieve 46.7 F1 for claim extraction and 91.4 F1 for law matching, albeit with some conceptual limitations. Furthermore, we discuss challenges of current machine learning approaches for legal language processing and their ability for complex legal reasoning tasks.

null

24,457

inproceedings

ali-etal-2022-constructing

Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules

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-1.51/

Ali, Basit and Pawar, Sachin and Palshikar, Girish and Singh, Rituraj

Proceedings of the Thirteenth Language Resources and Evaluation Conference

491--500

Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no publicly available dataset of these relations in the context of legal arguments expressed in court judgements. In this paper, we focus on automatically constructing such a dataset of Support and Attack relations between sentences in a court judgment with reasonable accuracy. We propose three sets of rules based on linguistic knowledge and distant supervision to identify such relations from Indian Supreme Court judgments. The first rule set is based on multiple discourse connectors, the second rule set is based on common semantic structures between argumentative sentences in a close neighbourhood, and the third rule set uses the information about the source of the argument. We also explore a BERT-based sentence pair classification model which is trained on this dataset. We release the dataset of 20506 sentence pairs - 10746 Support (precision 77.3{\%}) and 9760 Attack (precision 65.8{\%}). We believe that this dataset and the ideas explored in designing the linguistic rules and will boost the argumentation mining research for legal arguments.

null

24,458

inproceedings

paccosi-palmero-aprosio-2022-kind

{KIND}: an {I}talian Multi-Domain Dataset for Named Entity 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-1.52/

Paccosi, Teresa and Palmero Aprosio, Alessio

Proceedings of the Thirteenth Language Resources and Evaluation Conference

501--507

In this paper we present KIND, an Italian dataset for Named-entity recognition. It contains more than one million tokens with annotation covering three classes: person, location, and organization. The dataset (around 600K tokens) mostly contains manual gold annotations in three different domains (news, literature, and political discourses) and a semi-automatically annotated part. The multi-domain feature is the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest Italian NER dataset with manual gold annotations. It represents an important resource for the training of NER systems in Italian. Texts and annotations are freely downloadable from the Github repository.

null

24,459

inproceedings

mikhalkova-khlyupin-2022-russian

{R}ussian Jeopardy! Data Set for Question-Answering 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-1.53/

Mikhalkova, Elena and Khlyupin, Alexander A.

Proceedings of the Thirteenth Language Resources and Evaluation Conference

508--514

Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much valued in chat-bots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Ch-g-k. The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! (Own Game). We observe its linguistic features and the related QA-task. We conclude about perspectives of a QA challenge based on the collected data set.

null

24,460

inproceedings

hattasch-binnig-2022-know

Know Better {--} A Clickbait Resolving Challenge

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-1.54/

H{\"attasch, Benjamin and Binnig, Carsten

Proceedings of the Thirteenth Language Resources and Evaluation Conference

515--523

In this paper, we present a new corpus of clickbait articles annotated by university students along with a corresponding shared task: clickbait articles use a headline or teaser that hides information from the reader to make them curious to open the article. We therefore propose to construct approaches that can automatically extract the relevant information from such an article, which we call clickbait resolving. We show why solving this task might be relevant for end users, and why clickbait can probably not be defeated with clickbait detection alone. Additionally, we argue that this task, although similar to question answering and some automatic summarization approaches, needs to be tackled with specialized models. We analyze the performance of some basic approaches on this task and show that models fine-tuned on our data can outperform general question answering models, while providing a systematic approach to evaluate the results. We hope that the data set and the task will help in giving users tools to counter clickbait in the future.

null

24,461

inproceedings

freitag-etal-2022-valet

Valet: Rule-Based Information Extraction for Rapid Deployment

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-1.55/

Freitag, Dayne and Cadigan, John and Sasseen, Robert and Kalmar, Paul

Proceedings of the Thirteenth Language Resources and Evaluation Conference

524--533

We present VALET, a framework for rule-based information extraction written in Python. VALET departs from legacy approaches predicated on cascading finite-state transducers, instead offering direct support for mixing heterogeneous information{--}lexical, orthographic, syntactic, corpus-analytic{--}in a succinct syntax that supports context-free idioms. We show how a handful of rules suffices to implement sophisticated matching, and describe a user interface that facilitates exploration for development and maintenance of rule sets. Arguing that rule-based information extraction is an important methodology early in the development cycle, we describe an experiment in which a VALET model is used to annotate examples for a machine learning extraction model. While learning to emulate the extraction rules, the resulting model generalizes them, recognizing valid extraction targets the rules failed to detect.

null

24,462

inproceedings

sweers-etal-2022-negation

Negation Detection in {D}utch 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-1.56/

Sweers, Tom and Hendrickx, Iris and Strik, Helmer

Proceedings of the Thirteenth Language Resources and Evaluation Conference

534--542

Proper recognition and interpretation of negation signals in text or communication is crucial for any form of full natural language understanding. It is also essential for computational approaches to natural language processing. In this study we focus on negation detection in Dutch spoken human-computer conversations. Since there exists no Dutch (dialogue) corpus annotated for negation we have annotated a Dutch corpus sample to evaluate our method for automatic negation detection. We use transfer learning and trained NegBERT (an existing BERT implementation used for negation detection) on English data with multilingual BERT to detect negation in Dutch dialogues. Our results show that adding in-domain training material improves the results. We show that we can detect both negation cues and scope in Dutch dialogues with high precision and recall. We provide a detailed error analysis and discuss the effects of cross-lingual and cross-domain transfer learning on automatic negation detection.

null

24,463

inproceedings

cieri-etal-2022-reflections

Reflections on 30 Years of Language Resource Development and Sharing

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-1.57/

Cieri, Christopher and Liberman, Mark and Cho, Sunghye and Strassel, Stephanie and Fiumara, James and Wright, Jonathan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

543--550

The Linguistic Data Consortium was founded in 1992 to solve the problem that limitations in access to shareable data was impeding progress in Human Language Technology research and development. At the time, DARPA had adopted the common task research management paradigm to impose additional rigor on their programs by also providing shared objectives, data and evaluation methods. Early successes underscored the promise of this paradigm but also the need for a standing infrastructure to host and distribute the shared data. During LDC`s initial five year grant, it became clear that the demand for linguistic data could not easily be met by the existing providers and that a dedicated data center could add capacity first for data collection and shortly thereafter for annotation. The expanding purview required expansions of LDC`s technical infrastructure including systems support and software development. An open question for the center would be its role in other kinds of research beyond data development. Over its 30 years history, LDC has performed multiple roles ranging from neutral, independent data provider to multisite programs, to creator of exploratory data in tight collaboration with system developers, to research group focused on data intensive investigations.

null

24,464

inproceedings

mapelli-etal-2022-language

Language Resources to Support Language Diversity {--} the {ELRA} Achievements

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-1.58/

Mapelli, Val{\'e}rie and Arranz, Victoria and Choukri, Khalid and Mazo, H{\'e}l{\`e}ne

Proceedings of the Thirteenth Language Resources and Evaluation Conference

551--558

This article highlights ELRA`s latest achievements in the field of Language Resources (LRs) identification, sharing and production. It also reports on ELRA`s involvement in several national and international projects, as well as in the organization of events for the support of LRs and related Language Technologies, including for under-resourced languages. Over the past few years, ELRA, together with its operational agency ELDA, has continued to increase its catalogue offer of LRs, establishing worldwide partnerships for the production of various types of LRs (SMS, tweets, crawled data, MT aligned data, speech LRs, sentiment-based data, etc.). Through their consistent involvement in EU-funded projects, ELRA and ELDA have contributed to improve the access to multilingual information in the context of the pandemic, develop tools for the de-identification of texts in the legal and medical domains, support the EU eTranslation Machine Translation system, and set up a European platform providing access to both resources and services. In December 2019, ELRA co-organized the LT4All conference, whose main topics were Language Technologies for enabling linguistic diversity and multilingualism worldwide. Moreover, although LREC was cancelled in 2020, ELRA published the LREC 2020 proceedings for the Main conference and Workshops papers, and carried on its dissemination activities while targeting the new LREC edition for 2022.

null

24,465

inproceedings

kamocki-witt-2022-ethical

Ethical Issues in Language Resources and Language Technology {--} Tentative Categorisation

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-1.59/

Kamocki, Pawel and Witt, Andreas

Proceedings of the Thirteenth Language Resources and Evaluation Conference

559--563

Ethical issues in Language Resources and Language Technology are often invoked, but rarely discussed. This is at least partly because little work has been done to systematize ethical issues and principles applicable in the fields of Language Resources and Language Technology. This paper provides an overview of ethical issues that arise at different stages of Language Resources and Language Technology development, from the conception phase through the construction phase to the use phase. Based on this overview, the authors propose a tentative taxonomy of ethical issues in Language Resources and Language Technology, built around five principles: Privacy, Property, Equality, Transparency and Freedom. The authors hope that this tentative taxonomy will facilitate ethical assessment of projects in the field of Language Resources and Language Technology, and structure the discussion on ethical issues in this domain, which may eventually lead to the adoption of a universally accepted Code of Ethics of the Language Resources and Language Technology community.

null

24,466

inproceedings

ducel-etal-2022-name

Do we Name the Languages we Study? The {\#}{B}ender{R}ule in {LREC} and {ACL} 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-1.60/

Ducel, Fanny and Fort, Kar{\"en and Lejeune, Ga{\"el and Lepage, Yves

Proceedings of the Thirteenth Language Resources and Evaluation Conference

564--573

This article studies the application of the {\#}BenderRule in Natural Language Processing (NLP) articles according to two dimensions. Firstly, in a contrastive manner, by considering two major international conferences, LREC and ACL, and secondly, in a diachronic manner, by inspecting nearly 14,000 articles over a period of time ranging from 2000 to 2020 for LREC and from 1979 to 2020 for ACL. For this purpose, we created a corpus from LREC and ACL articles from the above-mentioned periods, from which we manually annotated nearly 1,000. We then developed two classifiers to automatically annotate the rest of the corpus. Our results show that LREC articles tend to respect the {\#}BenderRule (80 to 90{\%} of them respect it), whereas 30 to 40{\%} of ACL articles do not. Interestingly, over the considered periods, the results appear to be stable for the two conferences, even though a rebound in ACL 2020 could be a sign of the influence of the blog post about the {\#}BenderRule.

null

24,467

inproceedings

de-bruyne-etal-2022-aspect

Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas {I}nstagram Posts

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-1.61/

De Bruyne, Luna and Karimi, Akbar and De Clercq, Orphee and Prati, Andrea and Hoste, Veronique

Proceedings of the Thirteenth Language Resources and Evaluation Conference

574--580

While aspect-based sentiment analysis of user-generated content has received a lot of attention in the past years, emotion detection at the aspect level has been relatively unexplored. Moreover, given the rise of more visual content on social media platforms, we want to meet the ever-growing share of multimodal content. In this paper, we present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA). Additionally, we take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. The presented dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. Our preliminary experiments suggest that ABEA does not benefit from multimodal coreference resolution, and that aspect and emotion classification only requires textual information. However, when more specific information about the aspects is desired, image recognition could be essential.

null

24,468

inproceedings

roccabruna-etal-2022-multi

Multi-source Multi-domain Sentiment Analysis with {BERT}-based 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-1.62/

Roccabruna, Gabriel and Azzolin, Steve and Riccardi, Giuseppe

Proceedings of the Thirteenth Language Resources and Evaluation Conference

581--589

Sentiment analysis is one of the most widely studied tasks in natural language processing. While BERT-based models have achieved state-of-the-art results in this task, little attention has been given to its performance variability across class labels, multi-source and multi-domain corpora. In this paper, we present an improved state-of-the-art and comparatively evaluate BERT-based models for sentiment analysis on Italian corpora. The proposed model is evaluated over eight sentiment analysis corpora from different domains (social media, finance, e-commerce, health, travel) and sources (Twitter, YouTube, Facebook, Amazon, Tripadvisor, Opera and Personal Healthcare Agent) on the prediction of positive, negative and neutral classes. Our findings suggest that BERT-based models are confident in predicting positive and negative examples but not as much with neutral examples. We release the sentiment analysis model as well as a newly financial domain sentiment corpus.

null

24,469

inproceedings

muhammad-etal-2022-naijasenti

{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual 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-1.63/

Muhammad, Shamsuddeen Hassan and Adelani, David Ifeoluwa and Ruder, Sebastian and Ahmad, Ibrahim Sa{'}id and Abdulmumin, Idris and Bello, Bello Shehu and Choudhury, Monojit and Emezue, Chris Chinenye and Abdullahi, Saheed Salahudeen and Aremu, Anuoluwapo and Jorge, Al{\'i}pio and Brazdil, Pavel

Proceedings of the Thirteenth Language Resources and Evaluation Conference

590--602

Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria{---}Hausa, Igbo, Nigerian-Pidgin, and Yor{\`u}b{\'a}{---}consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptive fine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages.

null

24,470

inproceedings

etienne-etal-2022-psycho

A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse 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-1.64/

Etienne, Aline and Battistelli, Delphine and Lecorv{\'e}, Gw{\'e}nol{\'e}

Proceedings of the Thirteenth Language Resources and Evaluation Conference

603--612

This paper presents a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in French. The methodology introduced here emphasizes the necessity of clarifying the main concepts implied by the analysis of emotions as they are expressed in texts, before conducting a manual annotation campaign. After explaining whatentails a deeply linguistic perspective on emotion expression modeling, we present a few NLP works that share some common points with this perspective and meticulously compare our approach with them. We then highlight some interesting quantitative results observed on our annotated corpus. The most notable interactions are on the one hand between emotion expression modes and genres of texts, and on the other hand between emotion expression modes and emotional categories. These observation corroborate and clarify some of the results already mentioned in other NLP works on emotion annotation.

null

24,471

inproceedings

prost-2022-integrating

Integrating a Phrase Structure Corpus Grammar and a Lexical-Semantic Network: the {HOLINET} Knowledge Graph

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-1.65/

Prost, Jean-Philippe

Proceedings of the Thirteenth Language Resources and Evaluation Conference

613--622

In this paper we address the question of how to integrate grammar and lexical-semantic knowledge within a single and homogeneous knowledge graph. We introduce a graph modelling of grammar knowledge which enables its merging with a lexical-semantic network. Such an integrated representation is expected, for instance, to provide new material for language-related graph embeddings in order to model interactions between Syntax and Semantics. Our base model relies on a phrase structure grammar. The phrase structure is accounted for by both a Proof-Theoretical representation, through a Context-Free Grammar, and a Model-Theoretical one, through a constraint-based grammar. The constraint types colour the grammar layer with syntactic relationships such as Immediate Dominance, Linear Precedence, and more. We detail a creation process which infers the grammar layer from a corpus annotated in constituency and integrates it with a lexical-semantic network through a shared POS tagset. We implement the process, and experiment with the French Treebank and the JeuxDeMots lexical-semantic network. The outcome is the HOLINET knowledge graph.

null

24,472

inproceedings

ottolina-etal-2022-impact

On the Impact of Temporal Representations on Metaphor 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-1.66/

Ottolina, Giorgio and Palmonari, Matteo Luigi and Vimercati, Manuel and Alam, Mehwish

Proceedings of the Thirteenth Language Resources and Evaluation Conference

623--632

State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.

null

24,473

inproceedings

sileo-moens-2022-analysis

Analysis and Prediction of {NLP} Models via Task 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-1.67/

Sileo, Damien and Moens, Marie-Francine

Proceedings of the Thirteenth Language Resources and Evaluation Conference

633--647

Task embeddings are low-dimensional representations that are trained to capture task properties. In this paper, we propose MetaEval, a collection of 101 NLP tasks. We fit a single transformer to all MetaEval tasks jointly while conditioning it on learned embeddings. The resulting task embeddings enable a novel analysis of the space of tasks. We then show that task aspects can be mapped to task embeddings for new tasks without using any annotated examples. Predicted embeddings can modulate the encoder for zero-shot inference and outperform a zero-shot baseline on GLUE tasks. The provided multitask setup can function as a benchmark for future transfer learning research.

null

24,474

inproceedings

hazem-etal-2022-cross

Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data

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-1.68/

Hazem, Amir and Bouhandi, Merieme and Boudin, Florian and Daille, Beatrice

Proceedings of the Thirteenth Language Resources and Evaluation Conference

648--662

Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and an important basis for further natural language processing applications. Even with persistent improvements, ATE still exhibits weak results exacerbated by small training data inherent to specialized domain corpora. Recently, transformers-based deep neural models, such as BERT, have proven to be efficient in many downstream NLP tasks. However, no systematic evaluation of ATE has been conducted so far. In this paper, we run an extensive study on fine-tuning pre-trained BERT models for ATE. We propose strategies that empirically show BERT`s effectiveness using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. Experiments have been conducted on four specialized domains in three languages. The obtained results suggest that BERT can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE.

null

24,475

inproceedings

jurkschat-etal-2022-shot

Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate

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-1.69/

Jurkschat, Lena and Wiedemann, Gregor and Heinrich, Maximilian and Ruckdeschel, Mattes and Torge, Sunna

Proceedings of the Thirteenth Language Resources and Evaluation Conference

663--672

We approach aspect-based argument mining as a supervised machine learning task to classify arguments into semantically coherent groups referring to the same defined aspect categories. As an exemplary use case, we introduce the Argument Aspect Corpus - Nuclear Energy that separates arguments about the topic of nuclear energy into nine major aspects. Since the collection of training data for further aspects and topics is costly, we investigate the potential for current transformer-based few-shot learning approaches to accurately classify argument aspects. The best approach is applied to a British newspaper corpus covering the debate on nuclear energy over the past 21 years. Our evaluation shows that a stable prediction of shares of argument aspects in this debate is feasible with 50 to 100 training samples per aspect. Moreover, we see signals for a clear shift in the public discourse in favor of nuclear energy in recent years. This revelation of changing patterns of pro and contra arguments related to certain aspects over time demonstrates the potential of supervised argument aspect detection for tracking issue-specific media discourses.

null

24,476

inproceedings

jacobsen-etal-2022-mulve

{M}u{LVE}, A Multi-Language Vocabulary Evaluation Data Set

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-1.70/

Jacobsen, Anik and Mohtaj, Salar and M{\"oller, Sebastian

Proceedings of the Thirteenth Language Resources and Evaluation Conference

673--679

Vocabulary learning is vital to foreign language learning. Correct and adequate feedback is essential to successful and satisfying vocabulary training. However, many vocabulary and language evaluation systems perform on simple rules and do not account for real-life user learning data. This work introduces Multi-Language Vocabulary Evaluation Data Set (MuLVE), a data set consisting of vocabulary cards and real-life user answers, labeled indicating whether the user answer is correct or incorrect. The data source is user learning data from the Phase6 vocabulary trainer. The data set contains vocabulary questions in German and English, Spanish, and French as target language and is available in four different variations regarding pre-processing and deduplication. We experiment to fine-tune pre-trained BERT language models on the downstream task of vocabulary evaluation with the proposed MuLVE data set. The results provide outstanding results of {\ensuremath{>}} 95.5 accuracy and F2-score. The data set is available on the European Language Grid.

null

24,477

inproceedings

zilio-etal-2022-plod

{PLOD}: An Abbreviation Detection Dataset for Scientific Documents

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-1.71/

Zilio, Leonardo and Saadany, Hadeel and Sharma, Prashant and Kanojia, Diptesh and Or{\u{a}}san, Constantin

Proceedings of the Thirteenth Language Resources and Evaluation Conference

680--688

The detection and extraction of abbreviations from unstructured texts can help to improve the performance of Natural Language Processing tasks, such as machine translation and information retrieval. However, in terms of publicly available datasets, there is not enough data for training deep-neural-networks-based models to the point of generalising well over data. This paper presents PLOD, a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbreviations and their long forms. We performed manual validation over a set of instances and a complete automatic validation for this dataset. We then used it to generate several baseline models for detecting abbreviations and long forms. The best models achieved an F1-score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms. We release this dataset along with our code and all the models publicly at \url{https://github.com/surrey-nlp/PLOD-AbbreviationDetection}

null

24,478

inproceedings

adewumi-etal-2022-potential

Potential Idiomatic Expression ({PIE})-{E}nglish: Corpus for Classes of Idioms

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-1.72/

Adewumi, Tosin and Vadoodi, Roshanak and Tripathy, Aparajita and Nikolaido, Konstantina and Liwicki, Foteini and Liwicki, Marcus

Proceedings of the Thirteenth Language Resources and Evaluation Conference

689--696

We present a fairly large, Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work. To the best of the authors' knowledge, this is the first idioms corpus with classes of idioms beyond the literal and the general idioms classification. In particular, the following classes are labelled in the dataset: metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal. We obtain an overall inter-annotator agreement (IAA) score, between two independent annotators, of 88.89{\%}. Many past efforts have been limited in the corpus size and classes of samples but this dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). The corpus may also be extended by researchers to meet specific needs. The corpus has part of speech (PoS) tagging from the NLTK library. Classification experiments performed on the corpus to obtain a baseline and comparison among three common models, including the BERT model, give good results. We also make publicly available the corpus and the relevant codes for working with it for NLP tasks.

null

24,479

inproceedings

bexte-etal-2022-lespell

{L}e{S}pell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner 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-1.73/

Bexte, Marie and Laarmann-Quante, Ronja and Horbach, Andrea and Zesch, Torsten

Proceedings of the Thirteenth Language Resources and Evaluation Conference

697--706

Spellchecking text written by language learners is especially challenging because errors made by learners differ both quantitatively and qualitatively from errors made by already proficient learners. We introduce LeSpell, a multi-lingual (English, German, Italian, and Czech) evaluation data set of spelling mistakes in context that we compiled from seven underlying learner corpora. Our experiments show that existing spellcheckers do not work well with learner data. Thus, we introduce a highly customizable spellchecking component for the DKPro architecture, which improves performance in many settings.

null

24,480

inproceedings

seiffe-etal-2022-subjective

Subjective Text Complexity Assessment 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-1.74/

Seiffe, Laura and Kallel, Fares and M{\"oller, Sebastian and Naderi, Babak and Roller, Roland

Proceedings of the Thirteenth Language Resources and Evaluation Conference

707--714

For different reasons, text can be difficult to read and understand for many people, especially if the text`s language is too complex. In order to provide suitable text for the target audience, it is necessary to measure its complexity. In this paper we describe subjective experiments to assess the readability of German text. We compile a new corpus of sentences provided by a German IT service provider. The sentences are annotated with the subjective complexity ratings by two groups of participants, namely experts and non-experts for that text domain. We then extract an extensive set of linguistically motivated features that are supposedly interacting with complexity perception. We show that a linear regression model with a subset of these features can be a very good predictor of text complexity.

null

24,481

inproceedings

frick-etal-2022-querying

Querying Interaction Structure: Approaches to Overlap in Spoken Language Corpora

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-1.75/

Frick, Elena and Schmidt, Thomas and Helmer, Henrike

Proceedings of the Thirteenth Language Resources and Evaluation Conference

715--722

In this paper, we address two problems in indexing and querying spoken language corpora with overlapping speaker contributions. First, we look into how token distance and token precedence can be measured when multiple primary data streams are available and when transcriptions happen to be tokenized, but are not synchronized with the sound at the level of individual tokens. We propose and experiment with a speaker-based search mode that enables any speaker`s transcription tier to be the basic tokenization layer whereby the contributions of other speakers are mapped to this given tier. Secondly, we address two distinct methods of how speaker overlaps can be captured in the TEI-based ISO Standard for Spoken Language Transcriptions (ISO 24624:2016) and how they can be queried by MTAS {--} an open source Lucene-based search engine for querying text with multilevel annotations. We illustrate the problems, introduce possible solutions and discuss their benefits and drawbacks.

null

24,482

inproceedings

pezik-etal-2022-diabiz

{D}ia{B}iz {--} an Annotated Corpus of {P}olish Call Center Dialogs

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-1.76/

P{\k{e}}zik, Piotr and Krawentek, Gosia and Karasi{\'n}ska, Sylwia and Wilk, Pawe{\l} and Rybi{\'n}ska, Paulina and Cichosz, Anna and Peljak-{\L}api{\'n}ska, Angelika and Deckert, Miko{\l}aj and Adamczyk, Micha{\l}

Proceedings of the Thirteenth Language Resources and Evaluation Conference

723--726

This paper introduces DiaBiz, a large, annotated, multimodal corpus of Polish telephone conversations conducted in varied business settings, comprising 4036 call centre interactions from nine different domains, i.e. banking, energy services, telecommunications, insurance, medical care, debt collection, tourism, retail and car rental. The corpus was developed to boost the development of third-party speech recognition engines, dialog systems and conversational intelligence tools for Polish. Its current size amounts to nearly 410 hours of recordings and over 3 million words of transcribed speech. We present the structure of the corpus, data collection and transcription procedures, challenges of punctuating and truecasing speech transcripts, dialog structure annotation and discuss some of the ecological validity considerations involved in the development of such resources.

null

24,483

inproceedings

dargis-etal-2022-lava

{L}a{VA} {--} {L}atvian Language Learner 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-1.77/

Dar{\c{g}}is, Roberts and Auzi{\c{n}}a, Ilze and Kaija, Inga and Lev{\={a}}ne-Petrova, Krist{\={i}}ne and Pokratniece, Krist{\={i}}ne

Proceedings of the Thirteenth Language Resources and Evaluation Conference

727--731

This paper presents the Latvian Language Learner Corpus (LaVA) developed at the Institute of Mathematics and Computer Science, University of Latvia. LaVA corpus contains 1015 essays (190k tokens and 790k characters excluding whitespaces) from foreigners studying at Latvian higher education institutions and who are learning Latvian as a foreign language in the first or second semester, reaching the A1 (possibly A2) Latvian language proficiency level. The corpus has morphological and error annotations. Error analysis and the statistics of the LaVA corpus are also provided in the paper. The corpus is publicly available at: \url{http://www.korpuss.lv/id/LaVA}.

null

24,484

inproceedings

heafield-etal-2022-europat

The {E}uro{P}at Corpus: A Parallel Corpus of {E}uropean Patent Data

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-1.78/

Heafield, Kenneth and Farrow, Elaine and van der Linde, Jelmer and Ram{\'i}rez-S{\'a}nchez, Gema and Wiggins, Dion

Proceedings of the Thirteenth Language Resources and Evaluation Conference

732--740

We present the EuroPat corpus of patent-specific parallel data for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. The filtered parallel corpora range in size from 51 million sentences (Spanish-English) to 154k sentences (Croatian-English), with the unfiltered (raw) corpora being up to 2 times larger. Access to clean, high quality, parallel data in technical domains such as science, engineering, and medicine is needed for training neural machine translation systems for tasks like online dispute resolution and eProcurement. Our evaluation found that the addition of EuroPat data to a generic baseline improved the performance of machine translation systems on in-domain test data in German, Spanish, French, and Polish; and in translating patent data from Croatian to English. The corpus has been released under Creative Commons Zero, and is expected to be widely useful for training high-quality machine translation systems, and particularly for those targeting technical documents such as patents and contracts.

null

24,485

inproceedings

eder-etal-2022-beste

{\textquotedblleftBeste Gr{\"u{\sse, Maria Meyer{\textquotedblright {--- Pseudonymization of Privacy-Sensitive Information in Emails

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-1.79/

Eder, Elisabeth and Wiegand, Michael and Krieg-Holz, Ulrike and Hahn, Udo

Proceedings of the Thirteenth Language Resources and Evaluation Conference

741--752

The exploding amount of user-generated content has spurred NLP research to deal with documents from various digital communication formats (tweets, chats, emails, etc.). Using these texts as language resources implies complying with legal data privacy regulations. To protect the personal data of individuals and preclude their identification, we employ pseudonymization. More precisely, we identify those text spans that carry information revealing an individual`s identity (e.g., names of persons, locations, phone numbers, or dates) and subsequently substitute them with synthetically generated surrogates. Based on CodE Alltag, a German-language email corpus, we address two tasks. The first task is to evaluate various architectures for the automatic recognition of privacy-sensitive entities in raw data. The second task examines the applicability of pseudonymized data as training data for such systems since models learned on original data cannot be published for reasons of privacy protection. As outputs of both tasks, we, first, generate a new pseudonymized version of CodE Alltag compliant with the legal requirements of the General Data Protection Regulation (GDPR). Second, we make accessible a tagger for recognizing privacy-sensitive information in German emails and similar text genres, which is trained on already pseudonymized data.

null

24,486

inproceedings

schmeisser-nieto-etal-2022-criteria

Criteria for the Annotation of Implicit Stereotypes

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-1.80/

Schmeisser-Nieto, Wolfgang and Nofre, Montserrat and Taul{\'e}, Mariona

Proceedings of the Thirteenth Language Resources and Evaluation Conference

753--762

The growth of social media has brought with it a massive channel for spreading and reinforcing stereotypes. This issue becomes critical when the affected targets are minority groups such as women, the LGBT+ community and immigrants. Although from the perspective of computational linguistics, the detection of this kind of stereotypes is steadily improving, most stereotypes are expressed implicitly and identifying them automatically remains a challenge. One of the problems we found for tackling this issue is the lack of an operationalised definition of implicit stereotypes that would allow us to annotate consistently new corpora by characterising the different forms in which stereotypes appear. In this paper, we present thirteen criteria for annotating implicitness which were elaborated to facilitate the subjective task of identifying the presence of stereotypes. We also present NewsCom-Implicitness, a corpus of 1,911 sentences, of which 426 comprise explicit and implicit racial stereotypes. An experiment was carried out to evaluate the applicability of these criteria. The results indicate that different criteria obtain different inter-annotator agreement values and that there is a greater agreement when more criteria can be identified in one sentence.

null

24,487

inproceedings

klumpp-etal-2022-common

Common Phone: A Multilingual Dataset for Robust Acoustic Modelling

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-1.81/

Klumpp, Philipp and Arias, Tomas and P{\'e}rez-Toro, Paula Andrea and Noeth, Elmar and Orozco-Arroyave, Juan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

763--768

Current state of the art acoustic models can easily comprise more than 100 million parameters. This growing complexity demands larger training datasets to maintain a decent generalization of the final decision function. An ideal dataset is not necessarily large in size, but large with respect to the amount of unique speakers, utilized hardware and varying recording conditions. This enables a machine learning model to explore as much of the domain-specific input space as possible during parameter estimation. This work introduces Common Phone, a gender-balanced, multilingual corpus recorded from more than 76.000 contributors via Mozilla`s Common Voice project. It comprises around 116 hours of speech enriched with automatically generated phonetic segmentation. A Wav2Vec 2.0 acoustic model was trained with the Common Phone to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation. The architecture achieved a PER of 18.1 {\%} on the entire test set, computed with all 101 unique phonetic symbols, showing slight differences between the individual languages. We conclude that Common Phone provides sufficient variability and reliable phonetic annotation to help bridging the gap between research and application of acoustic models.

null

24,488

inproceedings

al-haff-etal-2022-curras

Curras + Baladi: Towards a {L}evantine 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-1.82/

Al-Haff, Karim and Jarrar, Mustafa and Hammouda, Tymaa and Zaraket, Fadi

Proceedings of the Thirteenth Language Resources and Evaluation Conference

769--778

This paper presents two-fold contributions: a full revision of the Palestinian morphologically annotated corpus (Curras), and a newly annotated Lebanese corpus (Baladi). Both corpora can be used as a more general Levantine corpus. Baladi consists of around 9.6K morphologically annotated tokens. Each token was manually annotated with several morphological features and using LDC`s SAMA lemmas and tags. The inter-annotator evaluation on most features illustrates 78.5{\%} Kappa and 90.1{\%} F1-Score. Curras was revised by refining all annotations for accuracy, normalization and unification of POS tags, and linking with SAMA lemmas. This revision was also important to ensure that both corpora are compatible and can help to bridge the nuanced linguistic gaps that exist between the two highly mutually intelligible dialects. Both corpora are publicly available through a web portal.

null

24,489

inproceedings

yamada-etal-2022-annotation

Annotation Study of {J}apanese Judgments on Tort for Legal Judgment Prediction with Rationales

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-1.83/

Yamada, Hiroaki and Tokunaga, Takenobu and Ohara, Ryutaro and Takeshita, Keisuke and Sumida, Mihoko

Proceedings of the Thirteenth Language Resources and Evaluation Conference

779--790

This paper describes a comprehensive annotation study on Japanese judgment documents in civil cases. We aim to build an annotated corpus designed for Legal Judgment Prediction (LJP), especially for torts. Our annotation scheme contains annotations of whether tort is accepted by judges as well as its corresponding rationales for explainability purpose. Our annotation scheme extracts decisions and rationales at character-level. Moreover, the scheme can capture the explicit causal relation between judge`s decisions and their corresponding rationales, allowing multiple decisions in a document. To obtain high-quality annotation, we developed an annotation scheme with legal experts, and confirmed its reliability by agreement studies with Krippendorff`s alpha metric. The result of the annotation study suggests the proposed annotation scheme can produce a dataset of Japanese LJP at reasonable reliability.

null

24,490

inproceedings

ruiter-etal-2022-placing

Placing {M}-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online

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-1.84/

Ruiter, Dana and Reiners, Liane and D{'}Sa, Ashwin Geet and Kleinbauer, Thomas and Fohr, Dominique and Illina, Irina and Klakow, Dietrich and Schemer, Christian and Monnier, Angeliki

Proceedings of the Thirteenth Language Resources and Evaluation Conference

791--804

Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label user comments as {\textquotedblleft}hate{\textquotedblright} or {\textquotedblleft}neutral{\textquotedblright}. This ignores the complex and subjective nature of HS, which limits the real-life applicability of classifiers trained on these corpora. In this study, we present the M-Phasis corpus, a corpus of {\textasciitilde}9k German and French user comments collected from migration-related news articles. It goes beyond the {\textquotedblleft}hate{\textquotedblright}-{\textquotedblleft}neutral{\textquotedblright} dichotomy and is instead annotated with 23 features, which in combination become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate. The annotations are performed by 4 native speakers per language and achieve high (0.77 {\ensuremath{<}}= k {\ensuremath{<}}= 1) inter-annotator agreements. Besides describing the corpus creation and presenting insights from a content, error and domain analysis, we explore its data characteristics by training several classification baselines.

null

24,491

inproceedings

lapshinova-koltunski-etal-2022-parcorfull2

{P}ar{C}or{F}ull2.0: a Parallel Corpus Annotated with Full Coreference

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-1.85/

Lapshinova-Koltunski, Ekaterina and Ferreira, Pedro Augusto and Lartaud, Elina and Hardmeier, Christian

Proceedings of the Thirteenth Language Resources and Evaluation Conference

805--813

In this paper, we describe ParCorFull2.0, a parallel corpus annotated with full coreference chains for multiple languages, which is an extension of the existing corpus ParCorFull (Lapshinova-Koltunski et al., 2018). Similar to the previous version, this corpus has been created to address translation of coreference across languages, a phenomenon still challenging for machine translation (MT) and other multilingual natural language processing (NLP) applications. The current version of the corpus that we present here contains not only parallel texts for the language pair English-German, but also for English-French and English-Portuguese, which are all major European languages. The new language pairs belong to the Romance languages. The addition of a new language group creates a need of extension not only in terms of texts added, but also in terms of the annotation guidelines. Both French and Portuguese contain structures not found in English and German. Moreover, Portuguese is a pro-drop language bringing even more systemic differences in the realisation of coreference into our cross-lingual resources. These differences cause problems for multilingual coreference resolution and machine translation. Our parallel corpus with full annotation of coreference will be a valuable resource with a variety of uses not only for NLP applications, but also for contrastive linguists and researchers in translation studies.

null

24,492

inproceedings

boritchev-amblard-2022-multi

A Multi-Party Dialogue Ressource in {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-1.86/

Boritchev, Maria and Amblard, Maxime

Proceedings of the Thirteenth Language Resources and Evaluation Conference

814--823

We presentDialogues in Games(DinG), a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, composed of long dialogues, to facilitate their study in the style of (Asher et al., 2016). In a general dialogue setting, participants share personal information, which makes it impossible to disseminate the resource freely and openly. In DinG, the attention of the participants is focused on the game, which prevents them from talking about themselves. In addition, we are conducting a study on the nature of the questions in dialogue, through annotation (Cruz Blandon et al., 2019), in order to develop more natural automatic dialogue systems

null

24,493

inproceedings

zaragoza-bernabeu-etal-2022-bicleaner

Bicleaner {AI}: Bicleaner Goes Neural

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-1.87/

Zaragoza-Bernabeu, Jaume and Ram{\'i}rez-S{\'a}nchez, Gema and Ba{\~n}{\'o}n, Marta and Ortiz Rojas, Sergio

Proceedings of the Thirteenth Language Resources and Evaluation Conference

824--831

This paper describes the experiments carried out during the development of the latest version of Bicleaner, named Bicleaner AI, a tool that aims at detecting noisy sentences in parallel corpora. The tool, which now implements a new neural classifier, uses state-of-the-art techniques based on pre-trained transformer-based language models fine-tuned on a binary classification task. After that, parallel corpus filtering is performed, discarding the sentences that have lower probability of being mutual translations. Our experiments, based on the training of neural machine translation (NMT) with corpora filtered using Bicleaner AI for two different scenarios, show significant improvements in translation quality compared to the previous version of the tool which implemented a classifier based on Extremely Randomized Trees.

null

24,494

inproceedings

katinskaia-etal-2022-semi

Semi-automatically Annotated Learner Corpus for {R}ussian

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-1.88/

Katinskaia, Anisia and Lebedeva, Maria and Hou, Jue and Yangarber, Roman

Proceedings of the Thirteenth Language Resources and Evaluation Conference

832--839

We present ReLCo{---} the Revita Learner Corpus{---}a new semi-automatically annotated learner corpus for Russian. The corpus was collected while several thousand L2 learners were performing exercises using the Revita language-learning system. All errors were detected automatically by the system and annotated by type. Part of the corpus was annotated manually{---}this part was created for further experiments on automatic assessment of grammatical correctness. The Learner Corpus provides valuable data for studying patterns of grammatical errors, experimenting with grammatical error detection and grammatical error correction, and developing new exercises for language learners. Automating the collection and annotation makes the process of building the learner corpus much cheaper and faster, in contrast to the traditional approach of building learner corpora. We make the data publicly available.

null

24,495

inproceedings

batsuren-etal-2022-unimorph

{U}ni{M}orph 4.0: {U}niversal {M}orphology

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-1.89/

Batsuren, Khuyagbaatar and Goldman, Omer and Khalifa, Salam and Habash, Nizar and Kiera{\'s}, Witold and Bella, G{\'a}bor and Leonard, Brian and Nicolai, Garrett and Gorman, Kyle and Ate, Yustinus Ghanggo and Ryskina, Maria and Mielke, Sabrina and Budianskaya, Elena and El-Khaissi, Charbel and Pimentel, Tiago and Gasser, Michael and Lane, William Abbott and Raj, Mohit and Coler, Matt and Samame, Jaime Rafael Montoya and Camaiteri, Delio Siticonatzi and Rojas, Esa{\'u} Zumaeta and L{\'o}pez Francis, Didier and Oncevay, Arturo and L{\'o}pez Bautista, Juan and Villegas, Gema Celeste Silva and Hennigen, Lucas Torroba and Ek, Adam and Guriel, David and Dirix, Peter and Bernardy, Jean-Philippe and Scherbakov, Andrey and Bayyr-ool, Aziyana and Anastasopoulos, Antonios and Zariquiey, Roberto and Sheifer, Karina and Ganieva, Sofya and Cruz, Hilaria and Karah{\'o}ǧa, Ritv{\'a}n and Markantonatou, Stella and Pavlidis, George and Plugaryov, Matvey and Klyachko, Elena and Salehi, Ali and Angulo, Candy and Baxi, Jatayu and Krizhanovsky, Andrew and Krizhanovskaya, Natalia and Salesky, Elizabeth and Vania, Clara and Ivanova, Sardana and White, Jennifer and Maudslay, Rowan Hall and Valvoda, Josef and Zmigrod, Ran and Czarnowska, Paula and Nikkarinen, Irene and Salchak, Aelita and Bhatt, Brijesh and Straughn, Christopher and Liu, Zoey and Washington, Jonathan North and Pinter, Yuval and Ataman, Duygu and Wolinski, Marcin and Suhardijanto, Totok and Yablonskaya, Anna and Stoehr, Niklas and Dolatian, Hossep and Nuriah, Zahroh and Ratan, Shyam and Tyers, Francis M. and Ponti, Edoardo M. and Aiton, Grant and Arora, Aryaman and Hatcher, Richard J. and Kumar, Ritesh and Young, Jeremiah and Rodionova, Daria and Yemelina, Anastasia and Andrushko, Taras and Marchenko, Igor and Mashkovtseva, Polina and Serova, Alexandra and Prud{'}hommeaux, Emily and Nepomniashchaya, Maria and Giunchiglia, Fausto and Chodroff, Eleanor and Hulden, Mans and Silfverberg, Miikka and McCarthy, Arya D. and Yarowsky, David and Cotterell, Ryan and Tsarfaty, Reut and Vylomova, Ekaterina

Proceedings of the Thirteenth Language Resources and Evaluation Conference

840--855

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

null

24,496

inproceedings

kalpakchi-boye-2022-textinator

Textinator: an Internationalized Tool for Annotation and Human Evaluation in Natural Language Processing and 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-1.90/

Kalpakchi, Dmytro and Boye, Johan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

856--866

We release an internationalized annotation and human evaluation bundle, called Textinator, along with documentation and video tutorials. Textinator allows annotating data for a wide variety of NLP tasks, and its user interface is offered in multiple languages, lowering the entry threshold for domain experts. The latter is, in fact, quite a rare feature among the annotation tools, that allows controlling for possible unintended biases introduced due to hiring only English-speaking annotators. We illustrate the rarity of this feature by presenting a thorough systematic comparison of Textinator to previously published annotation tools along 9 different axes (with internationalization being one of them). To encourage researchers to design their human evaluation before starting to annotate data, Textinator offers an easy-to-use tool for human evaluations allowing importing surveys with potentially hundreds of evaluation items in one click. We finish by presenting several use cases of annotation and evaluation projects conducted using pre-release versions of Textinator. The presented use cases do not represent Textinator`s full annotation or evaluation capabilities, and interested readers are referred to the online documentation for more information.

null

24,497

inproceedings

ollagnier-etal-2022-cyberagressionado

{C}yber{A}gression{A}do-v1: a Dataset of Annotated Online Aggressions in {F}rench Collected through a Role-playing 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-1.91/

Ollagnier, Ana{\"is and Cabrio, Elena and Villata, Serena and Blaya, Catherine

Proceedings of the Thirteenth Language Resources and Evaluation Conference

867--875

Over the past decades, the number of episodes of cyber aggression occurring online has grown substantially, especially among teens. Most solutions investigated by the NLP community to curb such online abusive behaviors consist of supervised approaches relying on annotated data extracted from social media. However, recent studies have highlighted that private instant messaging platforms are major mediums of cyber aggression among teens. As such interactions remain invisible due to the app privacy policies, very few datasets collecting aggressive conversations are available for the computational analysis of language. In order to overcome this limitation, in this paper we present the CyberAgressionAdo-V1 dataset, containing aggressive multiparty chats in French collected through a role-playing game in high-schools, and annotated at different layers. We describe the data collection and annotation phases, carried out in the context of a EU and a national research projects, and provide insightful analysis on the different types of aggression and verbal abuse depending on the targeted victims (individuals or communities) emerging from the collected data.

null

24,498

inproceedings

jahan-etal-2022-finnish

{F}innish Hate-Speech Detection on Social Media Using {CNN} and {F}in{BERT}

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-1.92/

Jahan, Md Saroar and Oussalah, Mourad and Arhab, Nabil

Proceedings of the Thirteenth Language Resources and Evaluation Conference

876--882

There has been a lot of research in identifying hate posts from social media because of their detrimental effects on both individuals and society. The majority of this research has concentrated on English, although one notices the emergence of multilingual detection tools such as multilingual-BERT (mBERT). However, there is a lack of hate speech datasets compared to English, and a multilingual pre-trained model often contains fewer tokens for other languages. This paper attempts to contribute to hate speech identification in Finnish by constructing a new hate speech dataset that is collected from a popular forum (Suomi24). Furthermore, we have experimented with FinBERT pre-trained model performance for Finnish hate speech detection compared to state-of-the-art mBERT and other practices. In addition, we tested the performance of FinBERT compared to fastText as embedding, which employed with Convolution Neural Network (CNN). Our results showed that FinBERT yields a 91.7{\%} accuracy and 90.8{\%} F1 score value, which outperforms all state-of-art models, including multilingual-BERT and CNN.

null

24,499

inproceedings

moon-etal-2022-empirical

Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing

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-1.93/

Moon, Hyeonseok and Park, Chanjun and Lee, Seolhwa and Seo, Jaehyung and Lee, Jungseob and Eo, Sugyeong and Lim, Heuiseok

Proceedings of the Thirteenth Language Resources and Evaluation Conference

883--891

Automatic post-editing (APE) refers to a research field that aims to automatically correct errors included in the translation sentences derived by the machine translation system. This study has several limitations, considering the data acquisition, because there is no official dataset for most language pairs. Moreover, the amount of data is restricted even for language pairs in which official data has been released, such as WMT. To solve this problem and promote universal APE research regardless of APE data existence, this study proposes a method for automatically generating APE data based on a noising scheme from a parallel corpus. Particularly, we propose a human mimicking errors-based noising scheme that considers a practical correction process at the human level. We propose a precise inspection to attain high performance, and we derived the optimal noising schemes that show substantial effectiveness. Through these, we also demonstrate that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. In addition, we propose a dynamic noise injection strategy that enables the acquisition of a robust error correction capability and demonstrated its effectiveness by comparative analysis. This study enables obtaining a high performance APE model without human-generated data and can promote universal APE research for all language pairs targeting English.

null

24,500

inproceedings

edmiston-etal-2022-domain

Domain Mismatch Doesn`t Always Prevent Cross-lingual Transfer 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-1.94/

Edmiston, Daniel and Keung, Phillip and Smith, Noah A.

Proceedings of the Thirteenth Language Resources and Evaluation Conference

892--899

Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have claimed that domain mismatch prevents cross-lingual transfer, and their results show that unsupervised bilingual lexicon induction (UBLI) and unsupervised neural machine translation (UNMT) do not work well when the underlying monolingual corpora come from different domains (e.g., French text from Wikipedia but English text from UN proceedings). In this work, we show how a simple initialization regimen can overcome much of the effect of domain mismatch in cross-lingual transfer. We pre-train word and contextual embeddings on the concatenated domain-mismatched corpora, and use these as initializations for three tasks: MUSE UBLI, UN Parallel UNMT, and the SemEval 2017 cross-lingual word similarity task. In all cases, our results challenge the conclusions of prior work by showing that proper initialization can recover a large portion of the losses incurred by domain mismatch.

null

24,501

inproceedings

papaioannou-etal-2022-cross

Cross-Lingual Knowledge Transfer for Clinical Phenotyping

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-1.95/

Papaioannou, Jens-Michalis and Grundmann, Paul and van Aken, Betty and Samaras, Athanasios and Kyparissidis, Ilias and Giannakoulas, George and Gers, Felix and Loeser, Alexander

Proceedings of the Thirteenth Language Resources and Evaluation Conference

900--909

Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-of-the-art models are mostly applicable to clinical notes written in English. We therefore investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language and have a small amount of in-domain data available. Our results reveal two strategies that outperform the state-of-the-art: Translation-based methods in combination with domain-specific encoders and cross-lingual encoders plus adapters. We find that these strategies perform especially well for classifying rare phenotypes and we advise on which method to prefer in which situation. Our results show that using multilingual data overall improves clinical phenotyping models and can compensate for data sparseness.

null

24,502

inproceedings

mcnamee-duh-2022-multilingual

The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text

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-1.96/

McNamee, Paul and Duh, Kevin

Proceedings of the Thirteenth Language Resources and Evaluation Conference

910--918

Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21{\%} was observed, demonstrating the utility of the corpus.

null

24,503

inproceedings

sato-etal-2022-multilingual

Multilingual and Multimodal Learning for {B}razilian {P}ortuguese

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-1.97/

Sato, J{\'u}lia and Caseli, Helena and Specia, Lucia

Proceedings of the Thirteenth Language Resources and Evaluation Conference

919--927

Humans constantly deal with multimodal information, that is, data from different modalities, such as texts and images. In order for machines to process information similarly to humans, they must be able to process multimodal data and understand the joint relationship between these modalities. This paper describes the work performed on the VTLM (Visual Translation Language Modelling) framework from (Caglayan et al., 2021) to test its generalization ability for other language pairs and corpora. We use the multimodal and multilingual corpus How2 (Sanabria et al., 2018) in three parallel streams with aligned English-Portuguese-Visual information to investigate the effectiveness of the model for this new language pair and in more complex scenarios, where the sentence associated with each image is not a simple description of it. Our experiments on the Portuguese-English multimodal translation task using the How2 dataset demonstrate the efficacy of cross-lingual visual pretraining. We achieved a BLEU score of 51.8 and a METEOR score of 78.0 on the test set, outperforming the MMT baseline by about 14 BLEU and 14 METEOR. The good BLEU and METEOR values obtained for this new language pair, regarding the original English-German VTLM, establish the suitability of the model to other languages.

null

24,504

inproceedings

jeuris-niehues-2022-libris2s

{L}ibri{S}2{S}: A {G}erman-{E}nglish Speech-to-Speech Translation 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-1.98/

Jeuris, Pedro and Niehues, Jan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

928--935

Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the area of speech-to-speech translation is still limited, although it is essential to overcome the language barrier. We believe that one of the limiting factors is the availability of appropriate training data. We address this issue by creating LibriS2S, to our knowledge the first publicly available speech-to-speech training corpus between German and English. For this corpus, we used independently created audio for German and English leading to an unbiased pronunciation of the text in both languages. This allows the creation of a new text-to-speech and speech-to-speech translation model that directly learns to generate the speech signal based on the pronunciation of the source language. Using this created corpus, we propose Text-to-Speech models based on the example of the recently proposed FastSpeech 2 model that integrates source language information. We do this by adapting the model to take information such as the pitch, energy or transcript from the source speech as additional input.

null

24,505

inproceedings

macketanz-etal-2022-linguistically

A Linguistically Motivated Test Suite to Semi-Automatically Evaluate {G}erman{--}{E}nglish Machine Translation Output

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-1.99/

Macketanz, Vivien and Avramidis, Eleftherios and Burchardt, Aljoscha and Wang, He and Ai, Renlong and Manakhimova, Shushen and Strohriegel, Ursula and M{\"oller, Sebastian and Uszkoreit, Hans

Proceedings of the Thirteenth Language Resources and Evaluation Conference

936--947

This paper presents a fine-grained test suite for the language pair German{--}English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions. We describe the creation and implementation of the test suite in detail, providing a full list of all categories and phenomena. Furthermore, we present various exemplary applications of our test suite that have been implemented in the past years, like contributions to the Conference of Machine Translation, the usage of the test suite and MT outputs for quality estimation, and the expansion of the test suite to the language pair Portuguese{--}English. We describe how we tracked the development of the performance of various systems MT systems over the years with the help of the test suite and which categories and phenomena are prone to resulting in MT errors. For the first time, we also make a large part of our test suite publicly available to the research community.

null

24,506

inproceedings

gogoulou-etal-2022-cross

Cross-lingual Transfer of Monolingual 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-1.100/

Gogoulou, Evangelia and Ekgren, Ariel and Isbister, Tim and Sahlgren, Magnus

Proceedings of the Thirteenth Language Resources and Evaluation Conference

948--955

Recent studies in cross-lingual learning using multilingual models have cast doubt on the previous hypothesis that shared vocabulary and joint pre-training are the keys to cross-lingual generalization. We introduce a method for transferring monolingual models to other languages through continuous pre-training and study the effects of such transfer from four different languages to English. Our experimental results on GLUE show that the transferred models outperform an English model trained from scratch, independently of the source language. After probing the model representations, we find that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in English.

null

24,507

inproceedings

petersen-frey-etal-2022-dataset

Dataset of Student Solutions to Algorithm and Data Structure Programming Assignments

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-1.101/

Petersen-Frey, Fynn and Soll, Marcus and Kobras, Louis and Johannsen, Melf and Kling, Peter and Biemann, Chris

Proceedings of the Thirteenth Language Resources and Evaluation Conference

956--962

We present a dataset containing source code solutions to algorithmic programming exercises solved by hundreds of Bachelor-level students at the University of Hamburg. These solutions were collected during the winter semesters 2019/2020, 2020/2021 and 2021/2022. The dataset contains a set of solutions to a total of 21 tasks written in Java as well as Python and a total of over 1500 individual solutions. All solutions were submitted through Moodle and the Coderunner plugin and passed a number of test cases (including randomized tests), such that they can be considered as working correctly. All students whose solutions are included in the dataset gave their consent into publishing their solutions. The solutions are pseudonymized with a random solution ID. Included in this paper is a short analysis of the dataset containing statistical data and highlighting a few anomalies (e.g. the number of solutions per task decreases for the last few tasks due to grading rules). We plan to extend the dataset with tasks and solutions from upcoming courses.

null

24,508

inproceedings

mondal-etal-2022-language

Language Patterns and Behaviour of the Peer Supporters in Multilingual Healthcare Conversational 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-1.102/

Mondal, Ishani and Bali, Kalika and Jain, Mohit and Choudhury, Monojit and O{'}Neill, Jacki and Ochieng, Millicent and Awori, Kagnoya and Ronen, Keshet

Proceedings of the Thirteenth Language Resources and Evaluation Conference

963--975

In this work, we conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp groups in Kenya comprising of youth living with HIV. Even though the language of communication for the group was predominantly English, we observe frequent use of Kiswahili, Sheng and code-mixing among the three languages. We present an analysis of language choice and its accommodation, different functions of code-mixing, and relationship between sentiment and code-mixing. To explore the effectiveness of off-the-shelf Language Technologies (LT) in such situations, we attempt to build a sentiment analyzer for this dataset. Our experiments demonstrate the challenges of developing LT and therefore effective interventions for such forums and languages. We provide recommendations for language resources that should be built to address these challenges.

null

24,509

inproceedings

yong-etal-2022-frame

Frame Shift Prediction

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-1.103/

Yong, Zheng Xin and Watson, Patrick D. and Timponi Torrent, Tiago and Czulo, Oliver and Baker, Collin

Proceedings of the Thirteenth Language Resources and Evaluation Conference

976--986

Frame shift is a cross-linguistic phenomenon in translation which results in corresponding pairs of linguistic material evoking different frames. The ability to predict frame shifts would enable (semi-)automatic creation of multilingual frame annotations and thus speeding up FrameNet creation through annotation projection. Here, we first characterize how frame shifts result from other linguistic divergences such as translational divergences and construal differences. Our analysis also shows that many pairs of frames in frame shifts are multi-hop away from each other in Berkeley FrameNet`s net-like configuration. Then, we propose the Frame Shift Prediction task and demonstrate that our graph attention networks, combined with auxiliary training, can learn cross-linguistic frame-to-frame correspondence and predict frame shifts.

null

24,510

inproceedings

bigi-etal-2022-clelfpc

{CL}e{L}f{PC}: a Large Open Multi-Speaker Corpus of {F}rench Cued 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-1.104/

Bigi, Brigitte and Zimmermann, Maryvonne and Andr{\'e}, Carine

Proceedings of the Thirteenth Language Resources and Evaluation Conference

987--994

Cued Speech is a communication system developed for deaf people to complement speechreading at the phonetic level with hands. This visual communication mode uses handshapes in different placements near the face in combination with the mouth movements of speech to make the phonemes of spoken language look different from each other. This paper describes CLeLfPC - Corpus de Lecture en Langue fran{\c{c}}aise Parl{\'e}e Compl{\'e}t{\'e}e, a corpus of French Cued Speech. It consists in about 4 hours of audio and HD video recordings of 23 participants. The recordings are 160 different isolated {\textquoteleft}CV' syllables repeated 5 times, 320 words or phrases repeated 2-3 times and about 350 sentences repeated 2-3 times. The corpus is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. It can be used for any further research or teaching purpose. The corpus includes orthographic transliteration and other phonetic annotations on 5 of the recorded topics, i.e. syllables, words, isolated sentences and a text. The early results are encouraging: it seems that 1/ the hand position has a high influence on the key audio duration; and 2/ the hand shape has not.

null

24,511

inproceedings

hernandez-mena-etal-2022-samromur

Samr{\'o}mur Children: An {I}celandic Speech 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-1.105/

Hernandez Mena, Carlos Daniel and Mollberg, David Erik and Borsk{\'y}, Michal and Gu{\dh}nason, J{\'o}n

Proceedings of the Thirteenth Language Resources and Evaluation Conference

995--1002

Samr{\'o}mur Children is an Icelandic speech corpus intended for the field of automatic speech recognition. It contains 131 hours of read speech from Icelandic children aged between 4 to 17 years. The test portion was meticulously selected to cover a wide range of ages as possible; we aimed to have exactly the same amount of data per age range. The speech was collected with the crowd-sourcing platform Samr{\'o}mur.is, which is inspired on the {\textquotedblleft}Mozilla`s Common Voice Project{\textquotedblright}. The corpus was developed within the framework of the {\textquotedblleft}Language Technology Programme for Icelandic 2019 {\ensuremath{-}} 2023{\textquotedblright}; the goal of the project is to make Icelandic available in language-technology applications. Samr{\'o}mur Children is the first corpus in Icelandic with children`s voices for public use under a Creative Commons license. Additionally, we present baseline experiments and results using Kaldi.

null

24,512

inproceedings

solberg-ortiz-2022-norwegian

The {N}orwegian Parliamentary Speech 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-1.106/

Solberg, Per Erik and Ortiz, Pablo

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1003--1008

The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9{\%} relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a {\textquotedblleft}democratizing{\textquotedblright} effects in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.

null

24,513

inproceedings

bentum-etal-2022-speech

A Speech Recognizer for {F}risian/{D}utch Council Meetings

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-1.107/

Bentum, Martijn and ten Bosch, Louis and van den Heuvel, Henk and Wills, Simone and van der Niet, Domenique and Dijkstra, Jelske and Van de Velde, Hans

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1009--1015

We developed a bilingual Frisian/Dutch speech recognizer for council meetings in Frysl{\^a}n (the Netherlands). During these meetings both Frisian and Dutch are spoken, and code switching between both languages shows up frequently. The new speech recognizer is based on an existing speech recognizer for Frisian and Dutch named FAME!, which was trained and tested on historical radio broadcasts. Adapting a speech recognizer for the council meeting domain is challenging because of acoustic background noise, speaker overlap and the jargon typically used in council meetings. To train the new recognizer, we used the radio broadcast materials utilized for the development of the FAME! recognizer and added newly created manually transcribed audio recordings of council meetings from eleven Frisian municipalities, the Frisian provincial council and the Frisian water board. The council meeting recordings consist of 49 hours of speech, with 26 hours of Frisian speech and 23 hours of Dutch speech. Furthermore, from the same sources, we obtained texts in the domain of council meetings containing 11 million words; 1.1 million Frisian words and 9.9 million Dutch words. We describe the methods used to train the new recognizer, report the observed word error rates, and perform an error analysis on remaining errors.

null

24,514

inproceedings

fukuda-etal-2022-elderly

Elderly Conversational Speech Corpus with Cognitive Impairment Test and Pilot Dementia Detection Experiment Using Acoustic Characteristics of Speech in {J}apanese 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-1.108/

Fukuda, Meiko and Nishimura, Ryota and Umezawa, Maina and Yamamoto, Kazumasa and Iribe, Yurie and Kitaoka, Norihide

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1016--1022

There is a need for a simple method of detecting early signs of dementia which is not burdensome to patients, since early diagnosis and treatment can often slow the advance of the disease. Several studies have explored using only the acoustic and linguistic information of conversational speech as diagnostic material, with some success. To accelerate this research, we recorded natural conversations between 128 elderly people living in four different regions of Japan and interviewers, who also administered the Hasegawa`s Dementia Scale-Revised (HDS-R), a cognitive impairment test. Using our elderly speech corpus and dementia test results, we propose an SVM-based screening method which can detect dementia using the acoustic features of conversational speech even when regional dialects are present. We accomplish this by omitting some acoustic features, to limit the negative effect of differences between dialects. When using our proposed method, a dementia detection accuracy rate of about 91{\%} was achieved for speakers from two regions. When speech from four regions was used in a second experiment, the discrimination rate fell to 76.6{\%}, but this may have been due to using only sentence-level acoustic features in the second experiment, instead of sentence and phoneme-level features as in the previous experiment. This is an on-going research project, and additional investigation is needed to understand differences in the acoustic characteristics of phoneme units in the conversational speech collected from these four regions, to determine whether the removal of formants and other features can improve the dementia detection rate.

null

24,515

inproceedings

kocabiyikoglu-etal-2022-spoken

A Spoken Drug Prescription Dataset in {F}rench for Spoken Language Understanding

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-1.109/

Kocabiyikoglu, Ali Can and Portet, Fran{\c{cois and Gibert, Prudence and Blanchon, Herv{\'e and Babouchkine, Jean-Marc and Gavazzi, Ga{\"etan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1023--1031

Spoken medical dialogue systems are increasingly attracting interest to enhance access to healthcare services and improve quality and traceability of patient care. In this paper, we focus on medical drug prescriptions acquired on smartphones through spoken dialogue. Such systems would facilitate the traceability of care and would free the clinicians' time. However, there is a lack of speech corpora to develop such systems since most of the related corpora are in text form and in English. To facilitate the research and development of spoken medical dialogue systems, we present, to the best of our knowledge, the first spoken medical drug prescriptions corpus, named PxNLU. It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in prescriptions. We also present some experiments that demonstrate the interest of this corpus for the evaluation and development of medical dialogue systems.

null

24,516

inproceedings

tejedor-garcia-etal-2022-towards

Towards an Open-Source {D}utch Speech Recognition System for the Healthcare Domain

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-1.110/

Tejedor-Garc{\'i}a, Cristian and van der Molen, Berrie and van den Heuvel, Henk and van Hessen, Arjan and Pieters, Toine

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1032--1039

The current largest open-source generic automatic speech recognition (ASR) system for Dutch, Kaldi{\_}NL, does not include a domain-specific healthcare jargon in the lexicon. Commercial alternatives (e.g., Google ASR system) are also not suitable for this purpose, not only because of the lexicon issue, but they do not safeguard privacy of sensitive data sufficiently and reliably. These reasons motivate that just a small amount of medical staff employs speech technology in the Netherlands. This paper proposes an innovative ASR training method developed within the Homo Medicinalis (HoMed) project. On the semantic level it specifically targets automatic transcription of doctor-patient consultation recordings with a focus on the use of medicines. In the first stage of HoMed, the Kaldi{\_}NL language model (LM) is fine-tuned with lists of Dutch medical terms and transcriptions of Dutch online healthcare news bulletins. Despite the acoustic challenges and linguistic complexity of the domain, we reduced the word error rate (WER) by 5.2{\%}. The proposed method could be employed for ASR domain adaptation to other domains with sensitive and special category data. These promising results allow us to apply this methodology on highly sensitive audiovisual recordings of patient consultations at the Netherlands Institute for Health Services Research (Nivel).

null

24,517

inproceedings

moutti-etal-2022-dataset

A Dataset for Speech Emotion Recognition in {G}reek Theatrical Plays

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-1.111/

Moutti, Maria and Eleftheriou, Sofia and Koromilas, Panagiotis and Giannakopoulos, Theodoros

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1040--1046

Machine learning methodologies can be adopted in cultural applications and propose new ways to distribute or even present the cultural content to the public. For instance, speech analytics can be adopted to automatically generate subtitles in theatrical plays, in order to (among other purposes) help people with hearing loss. Apart from a typical speech-to-text transcription with Automatic Speech Recognition (ASR), Speech Emotion Recognition (SER) can be used to automatically predict the underlying emotional content of speech dialogues in theatrical plays, and thus to provide a deeper understanding how the actors utter their lines. However, real-world datasets from theatrical plays are not available in the literature. In this work we present GreThE, the Greek Theatrical Emotion dataset, a new publicly available data collection for speech emotion recognition in Greek theatrical plays. The dataset contains utterances from various actors and plays, along with respective valence and arousal annotations. Towards this end, multiple annotators have been asked to provide their input for each speech recording and inter-annotator agreement is taken into account in the final ground truth generation. In addition, we discuss the results of some indicative experiments that have been conducted with machine and deep learning frameworks, using the dataset, along with some widely used databases in the field of speech emotion recognition.

null

24,518

inproceedings

piits-etal-2022-audiobook

Audiobook Dialogues as Training Data for Conversational Style Synthetic Voices

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-1.112/

Piits, Liisi and Pajupuu, Hille and Sahkai, Heete and Altrov, Rene and Ermus, Liis and Tamuri, Kairi and Hein, Indrek and Mihkla, Meelis and Kiissel, Indrek and M{\"annisalu, Egert and Suluste, Kristjan and Pajupuu, Jaan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1047--1053

Synthetic voices are increasingly used in applications that require a conversational speaking style, raising the question as to which type of training data yields the most suitable speaking style for such applications. This study compares voices trained on three corpora of equal size recorded by the same speaker: an audiobook character speech (dialogue) corpus, an audiobook narrator speech corpus, and a neutral-style sentence-based corpus. The voices were trained with three text-to-speech synthesisers: two hidden Markov model-based synthesisers and a neural synthesiser. An evaluation study tested the suitability of their speaking style for use in customer service voice chatbots. Independently of the synthesiser used, the voices trained on the character speech corpus received the lowest, and those trained on the neutral-style corpus the highest scores. However, the evaluation results may have been confounded by the greater acoustic variability, less balanced sentence length distribution, and poorer phonemic coverage of the character speech corpus, especially compared to the neutral-style corpus. Therefore, the next step will be the creation of a more uniform, balanced, and representative audiobook dialogue corpus, and the evaluation of its suitability for further conversational-style applications besides customer service chatbots.

null

24,519

inproceedings

wu-etal-2022-using

Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation

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-1.113/

Wu, Yaru and Suchanek, Fabian and Vasilescu, Ioana and Lamel, Lori and Adda-Decker, Martine

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1054--1060

Speech characteristics vary from speaker to speaker. While some variation phenomena are due to the overall communication setting, others are due to diastratic factors such as gender, provenance, age, and social background. The analysis of these factors, although relevant for both linguistic and speech technology communities, is hampered by the need to annotate existing corpora or to recruit, categorise, and record volunteers as a function of targeted profiles. This paper presents a methodology that uses a knowledge base to provide speaker-specific information. This can facilitate the enrichment of existing corpora with new annotations extracted from the knowledge base. The method also helps the large scale analysis by automatically extracting instances of speech variation to correlate with diastratic features. We apply our method to an over 120-hour corpus of broadcast speech in French and investigate variation patterns linked to reduction phenomena and/or specific to connected speech such as disfluencies. We find significant differences in speech rate, the use of filler words, and the rate of non-canonical realisations of frequent segments as a function of different professional categories and age groups.

null

24,520

inproceedings

li-etal-2022-phone

Phone Inventories and Recognition for Every 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-1.114/

Li, Xinjian and Metze, Florian and Mortensen, David R. and Black, Alan W and Watanabe, Shinji

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1061--1067

Identifying phone inventories is a crucial component in language documentation and the preservation of endangered languages. However, even the largest collection of phone inventory only covers about 2000 languages, which is only 1/4 of the total number of languages in the world. A majority of the remaining languages are endangered. In this work, we attempt to solve this problem by estimating the phone inventory for any language listed in Glottolog, which contains phylogenetic information regarding 8000 languages. In particular, we propose one probabilistic model and one non-probabilistic model, both using phylogenetic trees ({\textquotedblleft}language family trees{\textquotedblright}) to measure the distance between languages. We show that our best model outperforms baseline models by 6.5 F1. Furthermore, we demonstrate that, with the proposed inventories, the phone recognition model can be customized for every language in the set, which improved the PER (phone error rate) in phone recognition by 25{\%}.

null

24,521

inproceedings

roussis-etal-2022-constructing

Constructing Parallel Corpora from {COVID}-19 News using {M}edi{S}ys Metadata

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-1.115/

Roussis, Dimitrios and Papavassiliou, Vassilis and Sofianopoulos, Sokratis and Prokopidis, Prokopis and Piperidis, Stelios

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1068--1072

This paper presents a collection of parallel corpora generated by exploiting the COVID-19 related dataset of metadata created with the Europe Media Monitor (EMM) / Medical Information System (MediSys) processing chain of news articles. We describe how we constructed comparable monolingual corpora of news articles related to the current pandemic and used them to mine about 11.2 million segment alignments in 26 EN-X language pairs, covering most official EU languages plus Albanian, Arabic, Icelandic, Macedonian, and Norwegian. Subsets of this collection have been used in shared tasks (e.g. Multilingual Semantic Search, Machine Translation) aimed at accelerating the creation of resources and tools needed to facilitate access to information in the COVID-19 emergency situation.

null

24,522

inproceedings

zhang-etal-2022-distant

A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes

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-1.116/

Zhang, Dongxu and Mohan, Sunil and Torkar, Michaela and McCallum, Andrew

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1073--1082

We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label biomedical relation extraction models. Our dataset contains 80k biomedical research abstracts labeled with mentions of chemicals, diseases, and genes, portions of which human experts labeled with 18 types of biomedical relationships between these entities (intended for evaluation), and the remainder of which (intended for training) has been distantly labeled via the CTD database with approximately 78{\%} accuracy. In comparison to similar preexisting datasets, ours is both substantially larger and cleaner; it also includes annotations linking mentions to their entities. We also provide three baseline deep neural network relation extraction models trained and evaluated on our new dataset.

null

24,523

inproceedings

bardhan-etal-2022-drugehrqa

{D}rug{EHRQA}: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

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-1.117/

Bardhan, Jayetri and Colas, Anthony and Roberts, Kirk and Wang, Daisy Zhe

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1083--1097

This paper develops the first question answering dataset (DrugEHRQA) containing question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR). EHRs contain patient records, stored in structured tables and unstructured clinical notes. The information in structured and unstructured EHRs is not strictly disjoint: information may be duplicated, contradictory, or provide additional context between these sources. Our dataset has medication-related queries, containing over 70,000 question-answer pairs. To provide a baseline model and help analyze the dataset, we have used a simple model (MultimodalEHRQA) which uses the predictions of a modality selection network to choose between EHR tables and clinical notes to answer the questions. This is used to direct the questions to the table-based or text-based state-of-the-art QA model. In order to address the problem arising from complex, nested queries, this is the first time Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (RAT-SQL) has been used to test the structure of query templates in EHR data. Our goal is to provide a benchmark dataset for multi-modal QA systems, and to open up new avenues of research in improving question answering over EHR structured data by using context from unstructured clinical data.

null

24,524

inproceedings

verkijk-vossen-2022-efficiently

Efficiently and Thoroughly Anonymizing a Transformer Language Model for {D}utch Electronic Health Records: a Two-Step Method

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-1.118/

Verkijk, Stella and Vossen, Piek

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1098--1103

Neural Network (NN) architectures are used more and more to model large amounts of data, such as text data available online. Transformer-based NN architectures have shown to be very useful for language modelling. Although many researchers study how such Language Models (LMs) work, not much attention has been paid to the privacy risks of training LMs on large amounts of data and publishing them online. This paper presents a new method for anonymizing a language model by presenting the way in which MedRoBERTa.nl, a Dutch language model for hospital notes, was anonymized. The two-step method involves i) automatic anonymization of the training data and ii) semi-automatic anonymization of the LM`s vocabulary. Adopting the fill-mask task where the model predicts what tokens are most probable in a certain context, it was tested how often the model will predict a name in a context where a name should be. It was shown that it predicts a name-like token 0.2{\%} of the time. Any name-like token that was predicted was never the name originally present in the training data. By explaining how a LM trained on highly private real-world medical data can be published, we hope that more language resources will be published openly and responsibly so the scientific community can profit from them.

null

24,525

inproceedings

grobol-etal-2022-bertrade

{BERT}rade: Using Contextual Embeddings to Parse {O}ld {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-1.119/

Grobol, Lo{\"ic and Regnault, Mathilde and Ortiz Suarez, Pedro and Sagot, Beno{\^it and Romary, Laurent and Crabb{\'e, Benoit

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1104--1113

The successes of contextual word embeddings learned by training large-scale language models, while remarkable, have mostly occurred for languages where significant amounts of raw texts are available and where annotated data in downstream tasks have a relatively regular spelling. Conversely, it is not yet completely clear if these models are also well suited for lesser-resourced and more irregular languages. We study the case of Old French, which is in the interesting position of having relatively limited amount of available raw text, but enough annotated resources to assess the relevance of contextual word embedding models for downstream NLP tasks. In particular, we use POS-tagging and dependency parsing to evaluate the quality of such models in a large array of configurations, including models trained from scratch from small amounts of raw text and models pre-trained on other languages but fine-tuned on Medieval French data.

null

24,526

inproceedings

kanerva-ginter-2022-domain

Out-of-Domain Evaluation of {F}innish Dependency Parsing

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-1.120/

Kanerva, Jenna and Ginter, Filip

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1114--1124

The prevailing practice in the academia is to evaluate the model performance on in-domain evaluation data typically set aside from the training corpus. However, in many real world applications the data on which the model is applied may very substantially differ from the characteristics of the training data. In this paper, we focus on Finnish out-of-domain parsing by introducing a novel UD Finnish-OOD out-of-domain treebank including five very distinct data sources (web documents, clinical, online discussions, tweets, and poetry), and a total of 19,382 syntactic words in 2,122 sentences released under the Universal Dependencies framework. Together with the new treebank, we present extensive out-of-domain parsing evaluation utilizing the available section-level information from three different Finnish UD treebanks (TDT, PUD, OOD). Compared to the previously existing treebanks, the new Finnish-OOD is shown include sections more challenging for the general parser, creating an interesting evaluation setting and yielding valuable information for those applying the parser outside of its training domain.

null

24,527

inproceedings

gugliotta-dinarelli-2022-tarc

{TA}r{C}: {T}unisian {A}rabish Corpus, First complete release

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-1.121/

Gugliotta, Elisa and Dinarelli, Marco

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1125--1136

In this paper we present the final result of a project focused on Tunisian Arabic encoded in Arabizi, the Latin-based writing system for digital conversations. The project led to the realization of two integrated and independent tools: a linguistic corpus and a neural network architecture created to annotate the former with various levels of linguistic information (code-switching classification, transliteration, tokenization, POS-tagging, lemmatization). We discuss the choices made in terms of computational and linguistic methodology and the strategies adopted to improve our results. We report on the experiments performed in order to outline our research path. Finally, we explain the reasons why we believe in the potential of these tools for both computational and linguistic researches.

null

24,528

inproceedings

zabokrtsky-etal-2022-towards

Towards Universal Segmentations: {U}ni{S}egments 1.0

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-1.122/

{\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k and Bafna, Niyati and Bodn{\'a}r, Jan and Kyj{\'a}nek, Luk{\'a}{\v{s}} and Svoboda, Emil and {\v{S}}ev{\v{c}}{\'i}kov{\'a}, Magda and Vidra, Jon{\'a}{\v{s}}

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1137--1149

Our work aims at developing a multilingual data resource for morphological segmentation. We present a survey of 17 existing data resources relevant for segmentation in 32 languages, and analyze diversity of how individual linguistic phenomena are captured across them. Inspired by the success of Universal Dependencies, we propose a harmonized scheme for segmentation representation, and convert the data from the studied resources into this common scheme. Harmonized versions of resources available under free licenses are published as a collection called UniSegments 1.0.

null

24,529

inproceedings

moran-etal-2022-teddi

{T}e{DD}i Sample: Text Data Diversity Sample for Language Comparison and Multilingual {NLP}

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-1.123/

Moran, Steven and Bentz, Christian and Gutierrez-Vasques, Ximena and Pelloni, Olga and Samardzic, Tanja

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1150--1158

We present the TeDDi sample, a diversity sample of text data for language comparison and multilingual Natural Language Processing. The TeDDi sample currently features 89 languages based on the typological diversity sample in the World Atlas of Language Structures. It consists of more than 20k texts and is accompanied by open-source corpus processing tools. The aim of TeDDi is to facilitate text-based quantitative analysis of linguistic diversity. We describe in detail the TeDDi sample, how it was created, data availability, and its added value through for NLP and linguistic research.

null

24,530

inproceedings

bear-cook-2022-leveraging

Leveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations

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-1.124/

Bear, Diego and Cook, Paul

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1159--1166

Word embeddings (Mikolov et al., 2013; Pennington et al., 2014) have been used to bolster the performance of natural language processing systems in a wide variety of tasks, including information retrieval (Roy et al., 2018) and machine translation (Qi et al., 2018). However, approaches to learning word embeddings typically require large corpora of running text to learn high quality representations. For many languages, such resources are unavailable. This is the case for Wolastoqey, also known as Passamaquoddy-Maliseet, an endangered low-resource Indigenous language. As there exist no large corpora of running text for Wolastoqey, in this paper, we leverage a bilingual dictionary to learn Wolastoqey word embeddings by encoding their corresponding English definitions into vector representations using pretrained English word and sequence representation models. Specifically, we consider representations based on pretrained word2vec (Mikolov et al., 2013), RoBERTa (Liu et al., 2019) and sentence-BERT (Reimers and Gurevych, 2019) models. We evaluate these embeddings in word prediction tasks focused on part-of-speech, animacy, and transitivity; semantic clustering; and reverse dictionary search. In all evaluations we demonstrate that approaches using these embeddings outperform task-specific baselines, without requiring any language-specific training or fine-tuning.

null

24,531

inproceedings

wiechetek-etal-2022-unmasking

Unmasking the Myth of Effortless Big Data - Making an Open Source Multi-lingual Infrastructure and Building Language Resources from Scratch

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-1.125/

Wiechetek, Linda and Hiovain-Asikainen, Katri and Mikkelsen, Inga Lill Sigga and Moshagen, Sjur and Pirinen, Flammie and Trosterud, Trond and Gaup, B{\o}rre

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1167--1177

Machine learning (ML) approaches have dominated NLP during the last two decades. From machine translation and speech technology, ML tools are now also in use for spellchecking and grammar checking, with a blurry distinction between the two. We unmask the myth of effortless big data by illuminating the efforts and time that lay behind building a multi-purpose corpus with regard to collecting, mark-up and building from scratch. We also discuss what kind of language technology minority languages actually need, and to what extent the dominating paradigm has been able to deliver these tools. In this context we present our alternative to corpus-based language technology, which is knowledge-based language technology, and we show how this approach can provide language technology solutions for languages being outside the reach of machine learning procedures. We present a stable and mature infrastructure (GiellaLT) containing more than hundred languages and building a number of language technology tools that are useful for language communities.

null

24,532

inproceedings

liesenfeld-dingemanse-2022-building

Building and curating conversational corpora for diversity-aware language science and technology

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-1.126/

Liesenfeld, Andreas and Dingemanse, Mark

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1178--1192

We present an analysis pipeline and best practice guidelines for building and curating corpora of everyday conversation in diverse languages. Surveying language documentation corpora and other resources that cover 67 languages and varieties from 28 phyla, we describe the compilation and curation process, specify minimal properties of a unified format for interactional data, and develop methods for quality control that take into account turn-taking and timing. Two case studies show the broad utility of conversational data for (i) charting human interactional infrastructure and (ii) tracing challenges and opportunities for current ASR solutions. Linguistically diverse conversational corpora can provide new insights for the language sciences and stronger empirical foundations for language technology.

null

24,533

inproceedings

przybyl-etal-2022-epic

{EPIC} {U}d{S} - Creation and Applications of a Simultaneous Interpreting 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-1.127/

Przybyl, Heike and Lapshinova-Koltunski, Ekaterina and Menzel, Katrin and Fischer, Stefan and Teich, Elke

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1193--1200

In this paper, we describe the creation and annotation of EPIC UdS, a multilingual corpus of simultaneous interpreting for English, German and Spanish. We give an overview of the comparable and parallel, aligned corpus variants and explore various applications of the corpus. What makes EPIC UdS relevant is that it is one of the rare interpreting corpora that includes transcripts suitable for research on more than one language pair and on interpreting with regard to German. It not only contains transcribed speeches, but also rich metadata and fine-grained linguistic annotations tailored for diverse applications across a broad range of linguistic subfields.

null

24,534

inproceedings

green-etal-2022-development

Development of a Benchmark Corpus to Support Entity Recognition in Job Descriptions

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-1.128/

Green, Thomas and Maynard, Diana and Lin, Chenghua

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1201--1208

We present the development of a benchmark suite consisting of an annotation schema, training corpus and baseline model for Entity Recognition (ER) in job descriptions, published under a Creative Commons license. This was created to address the distinct lack of resources available to the community for the extraction of salient entities, such as skills, from job descriptions. The dataset contains 18.6k entities comprising five types (Skill, Qualification, Experience, Occupation, and Domain). We include a benchmark CRF-based ER model which achieves an F1 score of 0.59. Through the establishment of a standard definition of entities and training/testing corpus, the suite is designed as a foundation for future work on tasks such as the development of job recommender systems.

null

24,535

inproceedings

arrigo-etal-2022-camio

{CAMIO}: A Corpus for {OCR} in Multiple 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-1.129/

Arrigo, Michael and Strassel, Stephanie and King, Nolan and Tran, Thao and Mason, Lisa

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1209--1216

CAMIO (Corpus of Annotated Multilingual Images for OCR) is a new corpus created by Linguistic Data Consortium to serve as a resource to support the development and evaluation of optical character recognition (OCR) and related technologies for 35 languages across 24 unique scripts. The corpus comprises nearly 70,000 images of machine printed text, covering a wide variety of topics and styles, document domains, attributes and scanning/capture artifacts. Most images have been exhaustively annotated for text localization, resulting in over 2.3M line-level bounding boxes. For 13 of the 35 languages, 1250 images/language have been further annotated with orthographic transcriptions of each line plus specification of reading order, yielding over 2.4M tokens of transcribed text. The resulting annotations are represented in a comprehensive XML output format defined for this corpus. The paper discusses corpus design and implementation, challenges encountered, baseline performance results obtained on the corpus for text localization and OCR decoding, and plans for corpus publication.

null

24,536

inproceedings

wilkens-etal-2022-fabra

{FABRA}: {F}rench Aggregator-Based Readability Assessment toolkit

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-1.130/

Wilkens, Rodrigo and Alfter, David and Wang, Xiaoou and Pintard, Alice and Tack, Ana{\"is and Yancey, Kevin P. and Fran{\c{cois, Thomas

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1217--1233

In this paper, we present the FABRA: readability toolkit based on the aggregation of a large number of readability predictor variables. The toolkit is implemented as a service-oriented architecture, which obviates the need for installation, and simplifies its integration into other projects. We also perform a set of experiments to show which features are most predictive on two different corpora, and how the use of aggregators improves performance over standard feature-based readability prediction. Our experiments show that, for the explored corpora, the most important predictors for native texts are measures of lexical diversity, dependency counts and text coherence, while the most important predictors for foreign texts are syntactic variables illustrating language development, as well as features linked to lexical sophistication. FABRA: have the potential to support new research on readability assessment for French.

null

24,537

inproceedings

aicher-etal-2022-towards

Towards Building a Spoken Dialogue System for Argument Exploration

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-1.131/

Aicher, Annalena and Gerstenlauer, Nadine and Feustel, Isabel and Minker, Wolfgang and Ultes, Stefan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1234--1241

Speech interfaces for argumentative dialogue systems (ADS) are rather scarce. The complex task they pursue hinders the application of common natural language understanding (NLU) approaches in this domain. To address this issue we include an adaption of a recently introduced NLU framework tailored to argumentative tasks into a complete ADS. We evaluate the likeability and motivation of users to interact with the new system in a user study. Therefore, we compare it to a solid baseline utilizing a drop-down menu. The results indicate that the integration of a flexible NLU framework enables a far more natural and satisfying interaction with human users in real-time. Even though the drop-down menu convinces regarding its robustness, the willingness to use the new system is significantly higher. Hence, the featured NLU framework provides a sound basis to build an intuitive interface which can be extended to adapt its behavior to the individual user.

null

24,538

inproceedings

park-etal-2022-freetalky

{F}ree{T}alky: Don`t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based 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-1.132/

Park, Chanjun and Jang, Yoonna and Lee, Seolhwa and Park, Sungjin and Lim, Heuiseok

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1242--1248

We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robot NAO and various deep learning models. A persona-based dialogue system that is embedded in NAO provides an interesting and consistent multi-turn dialogue for users. Also, an grammar error correction system promotes improvement in grammar skills of the users. Thus, our system enables personalized learning based on persona dialogue and facilitates grammar learning of a user using grammar error feedback. Furthermore, we verified whether FreeTalky provides practical help in alleviating xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation.

null

24,539

inproceedings

hayashibe-2022-self

Self-Contained Utterance Description Corpus for {J}apanese Dialog

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-1.133/

Hayashibe, Yuta

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1249--1255

Often both an utterance and its context must be read to understand its intent in a dialog. Herein we propose a task, Self- Contained Utterance Description (SCUD), to describe the intent of an utterance in a dialog with multiple simple natural sentences without the context. If a task can be performed concurrently with high accuracy as the conversation continues such as in an accommodation search dialog, the operator can easily suggest candidates to the customer by inputting SCUDs of the customer`s utterances to the accommodation search system. SCUDs can also describe the transition of customer requests from the dialog log. We construct a Japanese corpus to train and evaluate automatic SCUD generation. The corpus consists of 210 dialogs containing 10,814 sentences. We conduct an experiment to verify that SCUDs can be automatically generated. Additionally, we investigate the influence of the amount of training data on the automatic generation performance using 8,200 additional examples.

null

24,540

inproceedings

huynh-etal-2022-dialcrowd

{D}ial{C}rowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit

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-1.134/

Huynh, Jessica and Chiang, Ting-Rui and Bigham, Jeffrey and Eskenazi, Maxine

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1256--1263

Dialog system developers need high-quality data to train, fine-tune and assess their systems. They often use crowdsourcing for this since it provides large quantities of data from many workers. However, the data may not be of sufficiently good quality. This can be due to the way that the requester presents a task and how they interact with the workers. This paper introduces DialCrowd 2.0 to help requesters obtain higher quality data by, for example, presenting tasks more clearly and facilitating effective communication with workers. DialCrowd 2.0 guides developers in creating improved Human Intelligence Tasks (HITs) and is directly applicable to the workflows used currently by developers and researchers.

null

24,541

inproceedings

goncalo-oliveira-etal-2022-brief

A Brief Survey of Textual Dialogue Corpora

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-1.135/

Gon{\c{c}}alo Oliveira, Hugo and Ferreira, Patr{\'i}cia and Martins, Daniel and Silva, Catarina and Alves, Ana

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1264--1274

Several dialogue corpora are currently available for research purposes, but they still fall short for the growing interest in the development of dialogue systems with their own specific requirements. In order to help those requiring such a corpus, this paper surveys a range of available options, in terms of aspects like speakers, size, languages, collection, annotations, and domains. Some trends are identified and possible approaches for the creation of new corpora are also discussed.

null

24,542

inproceedings

ruckert-etal-2022-unified

A Unified Approach to Entity-Centric Context Tracking in Social 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-1.136/

R{\"uckert, Ulrich and Sunkara, Srinivas and Rastogi, Abhinav and Prakash, Sushant and Khaitan, Pranav

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1275--1285

In human-human conversations, Context Tracking deals with identifying important entities and keeping track of their properties and relationships. This is a challenging problem that encompasses several subtasks such as slot tagging, coreference resolution, resolving plural mentions and entity linking. We approach this problem as an end-to-end modeling task where the conversational context is represented by an entity repository containing the entity references mentioned so far, their properties and the relationships between them. The repository is updated turn-by-turn, thus making training and inference computationally efficient even for long conversations. This paper lays the groundwork for an investigation of this framework in two ways. First, we release Contrack, a large scale human-human conversation corpus for context tracking with people and location annotations. It contains over 7000 conversations with an average of 11.8 turns, 5.8 entities and 15.2 references per conversation. Second, we open-source a neural network architecture for context tracking. Finally we compare this network to state-of-the-art approaches for the subtasks it subsumes and report results on the involved tradeoffs.

null

24,543

inproceedings

hudecek-etal-2022-unifying

DIASER: A Unifying View On Task-oriented Dialogue Annotation

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-1.137/

Hude{\v{c}}ek, Vojt{\v{e}}ch and Schaub, L{\'e}on-Paul and Stancl, Daniel and Paroubek, Patrick and Du{\v{s}}ek, Ond{\v{r}}ej

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1286--1296

Every model is only as strong as the data that it is trained on. In this paper, we present a new dataset, obtained by merging four publicly available annotated corpora for task-oriented dialogues in several domains (MultiWOZ 2.2, CamRest676, DSTC2 and Schema-Guided Dialogue Dataset). This way, we assess the feasibility of providing a unified ontology and annotation schema covering several domains with a relatively limited effort. We analyze the characteristics of the resulting dataset along three main dimensions: language, information content and performance. We focus on aspects likely to be pertinent for improving dialogue success, e.g. dialogue consistency. Furthermore, to assess the usability of this new corpus, we thoroughly evaluate dialogue generation performance under various conditions with the help of two prominent recent end-to-end dialogue models: MarCo and GPT-2. These models were selected as popular open implementations representative of the two main dimensions of dialogue modelling. While we did not observe a significant gain for dialogue state tracking performance, we show that using more training data from different sources can improve language modelling capabilities and positively impact dialogue flow (consistency). In addition, we provide the community with one of the largest open dataset for machine learning experiments.

null

24,544

inproceedings

origlia-etal-2022-multi

A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues 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-1.138/

Origlia, Antonio and Di Bratto, Martina and Di Maro, Maria and Mennella, Sabrina

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1297--1306

In dialogue analysis, characterising named entities in the domain of interest is relevant in order to understand how people are making use of them for argumentation purposes. The movie recommendation domain is a frequently considered case study for many applications and by linguistic studies and, since many different resources have been collected throughout the years to describe it, a single database combining all these data sources is a valuable asset for cross-disciplinary investigations. We propose an integrated graph-based structure of multiple resources, enriched with the results of the application of graph analytics approaches to provide an encompassing view of the domain and of the way people talk about it during the recommendation task. While we cannot distribute the final resource because of licensing issues, we share the code to assemble and process it once the reference data have been obtained from the original sources.

null

24,545

inproceedings

plaza-del-arco-etal-2022-share

{SHARE}: A Lexicon of Harmful Expressions by {S}panish Speakers

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-1.139/

Plaza-del-Arco, Flor Miriam and Parras Portillo, Ana Bel{\'e}n and L{\'o}pez {\'U}beda, Pilar and Gil, Beatriz and Mart{\'i}n-Valdivia, Mar{\'i}a-Teresa

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1307--1316

In this paper we present SHARE, a new lexical resource with 10,125 offensive terms and expressions collected from Spanish speakers. We retrieve this vocabulary using an existing chatbot developed to engage a conversation with users and collect insults via Telegram, named Fiero. This vocabulary has been manually labeled by five annotators obtaining a kappa coefficient agreement of 78.8{\%}. In addition, we leverage the lexicon to release the first corpus in Spanish for offensive span identification research named OffendES{\_}spans. Finally, we show the utility of our resource as an interpretability tool to explain why a comment may be considered offensive.

null

24,546

inproceedings

ylonen-2022-wiktextract

Wiktextract: {W}iktionary as Machine-Readable Structured Data

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-1.140/

Ylonen, Tatu

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1317--1325

We present a machine-readable structured data version of Wiktionary. Unlike previous Wiktionary extractions, the new extractor, Wiktextract, fully interprets and expands templates and Lua modules in Wiktionary. This enables it to perform a more complete, robust, and maintainable extraction. The extracted data is multilingual and includes lemmas, inflected forms, translations, etymology, usage examples, pronunciations (including URLs of sound files), lexical and semantic relations, and various morphological, syntactic, semantic, topical, and dialectal annotations. We extract all data from the English Wiktionary. Comparing against previous extractions from language-specific dictionaries, we find that its coverage for non-English languages often matches or exceeds the coverage in the language-specific editions, with the added benefit that all glosses are in English. The data is freely available and regularly updated, enabling anyone to add more data and correct errors by editing Wiktionary. The extracted data is in JSON format and designed to be easy to use by researchers, downstream resources, and application developers.

null

24,547

inproceedings

holmer-rennes-2022-nyllex

{N}y{LL}ex: A Novel Resource of {S}wedish Words Annotated with Reading Proficiency Level

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-1.141/

Holmer, Daniel and Rennes, Evelina

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1326--1331

What makes a text easy to read or not, depends on a variety of factors. One of the most prominent is, however, if the text contains easy, and avoids difficult, words. Deciding if a word is easy or difficult is not a trivial task, since it depends on characteristics of the word in itself as well as the reader, but it can be facilitated by the help of a corpus annotated with word frequencies and reading proficiency levels. In this paper, we present NyLLex, a novel lexical resource derived from books published by Sweden`s largest publisher for easy language texts. NyLLex consists of 6,668 entries, with frequency counts distributed over six reading proficiency levels. We show that NyLLex, with its novel source material aimed at individuals of different reading proficiency levels, can serve as a complement to already existing resources for Swedish.

null

24,548

inproceedings

uresova-etal-2022-making

Making a Semantic Event-type Ontology Multilingual

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-1.142/

Uresova, Zdenka and Zaczynska, Karolina and Bourgonje, Peter and Fu{\v{c}}{\'i}kov{\'a}, Eva and Rehm, Georg and Hajic, Jan

Proceedings of the Thirteenth Language Resources and Evaluation Conference

1332--1343

We present an extension of the SynSemClass Event-type Ontology, originally conceived as a bilingual Czech-English resource. We added German entries to the classes representing the concepts of the ontology. Having a different starting point than the original work (unannotated parallel corpus without links to a valency lexicon and, of course, different existing lexical resources), it was a challenge to adapt the annotation guidelines, the data model and the tools used for the original version. We describe the process and results of working in such a setup. We also show the next steps to adapt the annotation process, data structures and formats and tools necessary to make the addition of a new language in the future more smooth and efficient, and possibly to allow for various teams to work on SynSemClass extensions to many languages concurrently. We also present the latest release which contains the results of adding German, freely available for download as well as for online access.

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24,549