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

inproceedings

kevers-2022-coswid

{C}o{S}w{ID}, a Code Switching Identification Method Suitable for Under-Resourced Languages

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.15/

Kevers, Laurent

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

112--121

We propose a method for identifying monolingual textual segments in multilingual documents. It requires only a minimal number of linguistic resources {--} word lists and monolingual corpora {--} and can therefore be adapted to many under-resourced languages. Taking these languages into account when processing multilingual documents in NLP tools is important as it can contribute to the creation of essential textual resources. This language identification task {--} code switching detection being its most complex form {--} can also provide added value to various existing data or tools. Our research demonstrates that a language identification module performing well on short texts can be used to efficiently analyse a document through a sliding window. The results obtained for code switching identification {--} between 87.29{\%} and 97.97{\%} accuracy {--} are state-of-the-art, which is confirmed by the benchmarks performed on the few available systems that have been used on our test data.

null

22,748

inproceedings

resiandi-etal-2022-neural

A Neural Network Approach to Create {M}inangkabau-{I}ndonesia Bilingual Dictionary

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.16/

Resiandi, Kartika and Murakami, Yohei and Nasution, Arbi Haza

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

122--128

Indonesia has many varieties of ethnic languages, and most come from the same language family, namely Austronesian languages. Coming from that same language family, the words in Indonesian ethnic languages are very similar. However, there is research stating that Indonesian ethnic languages are endangered. Thus, to prevent that, we proposed to create a bilingual dictionary between ethnic languages using a neural network approach to extract transformation rules using character level embedding and the Bi-LSTM method in a sequence-to-sequence model. The model has an encoder and decoder. The encoder functions read the input sequence, character by character, generate context, then extract a summary of the input. The decoder will produce an output sequence where every character in each time-step and the next character that comes out are affected by the previous character. The current case for experiment translation focuses on Minangkabau and Indonesian languages with 13761-word pairs. For evaluating the model`s performance, 5-Fold Cross-Validation is used.

null

22,749

inproceedings

lambrecht-etal-2022-machine

Machine Translation from {S}tandard {G}erman to Alemannic Dialects

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.17/

Lambrecht, Louisa and Schneider, Felix and Waibel, Alexander

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

129--136

Machine translation has been researched using deep neural networks in recent years. These networks require lots of data to learn abstract representations of the input stored in continuous vectors. Dialect translation has become more important since the advent of social media. In particular, when dialect speakers and standard language speakers no longer understand each other, machine translation is of rising concern. Usually, dialect translation is a typical low-resourced language setting facing data scarcity problems. Additionally, spelling inconsistencies due to varying pronunciations and the lack of spelling rules complicate translation. This paper presents the best-performing approaches to handle these problems for Alemannic dialects. The results show that back-translation and conditioning on dialectal manifestations achieve the most remarkable enhancement over the baseline. Using back-translation, a significant gain of +4.5 over the strong transformer baseline of 37.3 BLEU points is accomplished. Differentiating between several Alemannic dialects instead of treating Alemannic as one dialect leads to substantial improvements: Multi-dialectal translation surpasses the baseline on the dialectal test sets. However, training individual models outperforms the multi-dialectal approach. There, improvements range from 7.5 to 10.6 BLEU points over the baseline depending on the dialect.

null

22,750

inproceedings

destaw-etal-2022-question

Question Answering Classification for {A}mharic Social Media Community Based Questions

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.18/

Belay, Tadesse Destaw and Yimam, Seid Muhie and Ayele, Abinew and Biemann, Chris

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

137--145

In this work, we build a Question Answering (QA) classification dataset from a social media platform, namely the Telegram public channel called @AskAnythingEthiopia. The channel has more than 78k subscribers and has existed since May 31, 2019. The platform allows asking questions that belong to various domains, like politics, economics, health, education, and so on. Since the questions are posed in a mixed-code, we apply different strategies to pre-process the dataset. Questions are posted in Amharic, English, or Amharic but in a Latin script. As part of the pre-processing tools, we build a Latin to Ethiopic Script transliteration tool. We collect 8k Amharic and 24K transliterated questions and develop deep learning-based questions answering classifiers that attain as high as an F-score of 57.29 in 20 different question classes or categories. The datasets and pre-processing scripts are open-sourced to facilitate further research on the Amharic community-based question answering.

null

22,751

inproceedings

adegbola-2022-automatic

Automatic Detection of Morphological Processes in the {Y}or{\`u}b{\'a} Language

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.19/

Adegbola, Tunde

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

146--154

Automatic morphology induction is important for computational processing of natural language. In resource-scarce languages in particular, it offers the possibility of supplementing data-driven strategies of Natural Language Processing with morphological rules that may cater for out-of-vocabulary words. Unfortunately, popular approaches to unsupervised morphology induction do not work for some of the most productive morphological processes of the Yor{\`u}b{\'a} language. To the best of our knowledge, the automatic induction of such morphological processes as full and partial reduplication, infixation, interfixation, compounding and other morphological processes, particularly those based on the affixation of stem-derived morphemes have not been adequately addressed in the literature. This study proposes a method for the automatic detection of stem-derived morphemes in Yor{\`u}b{\'a}. Words in a Yor{\`u}b{\'a} lexicon of 14,670 word-tokens were clustered around {\textquotedblleft}word-labels{\textquotedblright}. A word-label is a textual proxy of the patterns imposed on words by the morphological processes through which they were formed. Results confirm a conjectured significant difference between the predicted and observed probabilities of word-labels motivated by stem-derived morphemes. This difference was used as basis for automatic identification of words formed by the affixation of stem-derived morphemes. Keywords: Unsupervised Morphology Induction, Recurrent Partials, Recurrent Patterns, Stem-derived Morphemes, Word-labels.

null

22,752

inproceedings

bear-cook-2022-evaluating

Evaluating Unsupervised Approaches to Morphological Segmentation for Wolastoqey

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.20/

Bear, Diego and Cook, Paul

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

155--160

Finite-state approaches to morphological analysis have been shown to improve the performance of natural language processing systems for polysynthetic languages, in-which words are generally composed of many morphemes, for tasks such as language modelling (Schwartz et al., 2020). However, finite-state morphological analyzers are expensive to construct and require expert knowledge of a language`s structure. Currently, there is no broad-coverage finite-state model of morphology for Wolastoqey, also known as Passamaquoddy-Maliseet, an endangered low-resource Algonquian language. As this is the case, in this paper, we investigate using two unsupervised models, MorphAGram and Morfessor, to obtain morphological segmentations for Wolastoqey. We train MorphAGram and Morfessor models on a small corpus of Wolastoqey words and evaluate using two an notated datasets. Our results indicate that MorphAGram outperforms Morfessor for morphological segmentation of Wolastoqey.

null

22,753

inproceedings

cortis-davis-2022-baseline

Baseline {E}nglish and {M}altese-{E}nglish Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.21/

Cortis, Keith and Davis, Brian

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

161--168

This paper presents baseline classification models for subjectivity detection, sentiment analysis, emotion analysis, sarcasm detection, and irony detection. All models are trained on user-generated content gathered from newswires and social networking services, in three different languages: English {---a high-resourced language, Maltese {---a low-resourced language, and Maltese-English {---a code-switched language. Traditional supervised algorithms namely, Support Vector Machines, Na{\"ive Bayes, Logistic Regression, Decision Trees, and Random Forest, are used to build a baseline for each classification task, namely subjectivity, sentiment polarity, emotion, sarcasm, and irony. Baseline models are established at a monolingual (English) level and at a code-switched level (Maltese-English). Results obtained from all the classification models are presented.

null

22,754

inproceedings

hiovain-asikainen-moshagen-2022-building

Building Open-source Speech Technology for Low-resource Minority Languages with {S}{\'a}{M}i as an Example {--} Tools, Methods and Experiments

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.22/

Hiovain-Asikainen, Katri and Moshagen, Sjur

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

169--175

This paper presents a work-in-progress report of an open-source speech technology project for indigenous Sami languages. A less detailed description of this work has been presented in a more general paper about the whole GiellaLT language infrastructure, submitted to the LREC 2022 main conference. At this stage, we have designed and collected a text corpus specifically for developing speech technology applications, namely Text-to-speech (TTS) and Automatic speech recognition (ASR) for the Lule and North Sami languages. We have also piloted and experimented with different speech synthesis technologies using a miniature speech corpus as well as developed tools for effective processing of large spoken corpora. Additionally, we discuss effective and mindful use of the speech corpus and also possibilities to use found/archive materials for training an ASR model for these languages.

null

22,755

inproceedings

singh-etal-2022-investigating

Investigating the Quality of Static Anchor Embeddings from Transformers for Under-Resourced Languages

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.23/

Singh, Pranaydeep and De Clercq, Orphee and Lefever, Els

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

176--184

This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al. (2019) for English and a low-resourced language, namely Hindi. For the sake of comparison, we also evaluate the approach on three very different higher-resourced languages, viz. Dutch, Russian and Chinese. Initially designed for ELMo embeddings, we analyze the approach for the more recent BERT family of transformers for a variety of tasks, both mono and cross-lingual. The results largely prove that like most other cross-lingual transfer approaches, the static anchor approach is underwhelming for the low-resource language, while performing adequately for the higher resourced ones. We attempt to provide insights into both the quality of the anchors, and the performance for low-shot cross-lingual transfer to better understand this performance gap. We make the extracted anchors and the modified train and test sets available for future research at \url{https://github.com/pranaydeeps/Vyaapak}

null

22,756

inproceedings

merzhevich-ferraz-gerardi-2022-introducing

Introducing {Y}aku{T}oolkit. {Y}akut Treebank and Morphological Analyzer.

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.24/

Merzhevich, Tatiana and Ferraz Gerardi, Fabr{\'i}cio

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

185--188

This poster presents the first publicly available treebank of Yakut, a Turkic language spoken in Russia, and a morphological analyzer for this language. The treebank was annotated following the Universal Dependencies (UD) framework and the mor- phological analyzer can directly access and use its data. Yakut is an under-represented language whose prominence can be raised by making reliably annotated data and NLP tools that could process it freely accessible. The publication of both the treebank and the analyzer serves this purpose with the prospect of evolving into a benchmark for the development of NLP online tools for other languages of the Turkic family in the future.

null

22,757

inproceedings

abdulrahman-hassani-2022-language

A Language Model for Spell Checking of Educational Texts in {K}urdish ({S}orani)

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.25/

Abdulrahman, Roshna and Hassani, Hossein

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

189--198

Spell checkers are an integrated feature of most software applications handling text inputs. When we write an email or compile a report on a desktop or a smartphone editor, a spell checker could be activated that assists us to write more correctly. However, this assistance does not exist for all languages equally. The Kurdish language, which still is considered a less-resourced language, currently lacks spell checkers for its various dialects. We present a trigram language model for the Sorani dialect of the Kurdish language that is created using educational text. We also showcase a spell checker for the Sorani dialect of Kurdish that can assist in writing texts in the Persian/Arabic script. The spell checker was developed as a testing environment for the language model. Primarily, we use the probabilistic method and our trigram language model with Stupid Backoff smoothing for the spell checking algorithm. Our spell checker has been trained on the KTC (Kurdish Textbook Corpus) dataset. Hence the system aims at assisting spell checking in the related context. We test our approach by developing a text processing environment that checks for spelling errors on a word and context basis. It suggests a list of corrections for misspelled words. The developed spell checker shows 88.54{\%} accuracy on the texts in the related context and it has an F1 score of 43.33{\%}, and the correct suggestion has an 85{\%} chance of being in the top three positions of the corrections.

null

22,758

inproceedings

salaev-etal-2022-simreluz

{S}im{R}el{U}z: Similarity and Relatedness Scores as a Semantic Evaluation Dataset for {U}zbek Language

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.26/

Salaev, Ulugbek and Kuriyozov, Elmurod and G{\'o}mez-Rodr{\'i}guez, Carlos

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

199--206

Semantic relatedness between words is one of the core concepts in natural language processing, thus making semantic evaluation an important task. In this paper, we present a semantic model evaluation dataset: SimRelUz - a collection of similarity and relatedness scores of word pairs for the low-resource Uzbek language. The dataset consists of more than a thousand pairs of words carefully selected based on their morphological features, occurrence frequency, semantic relation, as well as annotated by eleven native Uzbek speakers from different age groups and gender. We also paid attention to the problem of dealing with rare words and out-of-vocabulary words to thoroughly evaluate the robustness of semantic models.

null

22,759

inproceedings

anastasiou-2022-enrich4all

{ENRICH}4{ALL}: A First {L}uxembourgish {BERT} Model for a Multilingual Chatbot

Melero, Maite and Sakti, Sakriani and Soria, Claudia

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.sigul-1.27/

Anastasiou, Dimitra

Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

207--212

Machine Translation (MT)-empowered chatbots are not established yet, however, we see an amazing future breaking language barriers and enabling conversation in multiple languages without time-consuming language model building and training, particularly for under-resourced languages. In this paper we focus on the under-resourced Luxembourgish language. This article describes the experiments we have done with a dataset containing administrative questions that we have manually created to offer BERT QA capabilities to a multilingual chatbot. The chatbot supports visual dialog flow diagram creation (through an interface called BotStudio) in which a dialog node manages the user question at a specific step. Dialog nodes can be matched to the user`s question by using a BERT classification model which labels the question with a dialog node label.

null

22,760

inproceedings

de-varda-zamparelli-2022-multilingualism

Multilingualism Encourages Recursion: a Transfer Study with m{BERT}

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.1/

De Varda, Andrea and Zamparelli, Roberto

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

1--10

The present work constitutes an attempt to investigate the relational structures learnt by mBERT, a multilingual transformer-based network, with respect to different cross-linguistic regularities proposed in the fields of theoretical and quantitative linguistics. We pursued this objective by relying on a zero-shot transfer experiment, evaluating the model`s ability to generalize its native task to artificial languages that could either respect or violate some proposed language universal, and comparing its performance to the output of BERT, a monolingual model with an identical configuration. We created four artificial corpora through a Probabilistic Context-Free Grammar by manipulating the distribution of tokens and the structure of their dependency relations. We showed that while both models were favoured by a Zipfian distribution of the tokens and by the presence of head-dependency type structures, the multilingual transformer network exhibited a stronger reliance on hierarchical cues compared to its monolingual counterpart.

null

10.18653/v1/2022.sigtyp-1.1

null

22,762

inproceedings

nikolaev-pado-2022-word

Word-order Typology in Multilingual {BERT}: A Case Study in Subordinate-Clause Detection

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.2/

Nikolaev, Dmitry and Pado, Sebastian

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

11--21

The capabilities and limitations of BERT and similar models are still unclear when it comes to learning syntactic abstractions, in particular across languages. In this paper, we use the task of subordinate-clause detection within and across languages to probe these properties. We show that this task is deceptively simple, with easy gains offset by a long tail of harder cases, and that BERT`s zero-shot performance is dominated by word-order effects, mirroring the SVO/VSO/SOV typology.

null

10.18653/v1/2022.sigtyp-1.2

null

22,763

inproceedings

hartung-etal-2022-typological

Typological Word Order Correlations with Logistic Brownian Motion

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.3/

Hartung, Kai and J{\"ager, Gerhard and Gr{\"ottrup, S{\"oren and Georges, Munir

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

22--26

In this study we address the question to what extent syntactic word-order traits of different languages have evolved under correlation and whether such dependencies can be found universally across all languages or restricted to specific language families. To do so, we use logistic Brownian Motion under a Bayesian framework to model the trait evolution for 768 languages from 34 language families. We test for trait correlations both in single families and universally over all families. Separate models reveal no universal correlation patterns and Bayes Factor analysis of models over all covered families also strongly indicate lineage specific correlation patters instead of universal dependencies.

null

10.18653/v1/2022.sigtyp-1.3

null

22,764

inproceedings

otmakhova-etal-2022-cross

Cross-linguistic Comparison of Linguistic Feature Encoding in {BERT} Models for Typologically Different Languages

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.4/

Otmakhova, Yulia and Verspoor, Karin and Lau, Jey Han

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

27--35

Though recently there have been an increased interest in how pre-trained language models encode different linguistic features, there is still a lack of systematic comparison between languages with different morphology and syntax. In this paper, using BERT as an example of a pre-trained model, we compare how three typologically different languages (English, Korean, and Russian) encode morphology and syntax features across different layers. In particular, we contrast languages which differ in a particular aspect, such as flexibility of word order, head directionality, morphological type, presence of grammatical gender, and morphological richness, across four different tasks.

null

10.18653/v1/2022.sigtyp-1.4

null

22,765

inproceedings

talamo-2022-tweaking

Tweaking {UD} Annotations to Investigate the Placement of Determiners, Quantifiers and Numerals in the Noun Phrase

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.5/

Talamo, Luigi

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

36--41

We describe a methodology to extract with finer accuracy word order patterns from texts automatically annotated with Universal Dependency (UD) trained parsers. We use the methodology to quantify the word order entropy of determiners, quantifiers and numerals in ten Indo-European languages, using UD-parsed texts from a parallel corpus of prosaic texts. Our results suggest that the combinations of different UD annotation layers, such as UD Relations, Universal Parts of Speech and lemma, and the introduction of language-specific lists of closed-category lemmata has the two-fold effect of improving the quality of analysis and unveiling hidden areas of variability in word order patterns.

null

10.18653/v1/2022.sigtyp-1.5

null

22,766

inproceedings

guo-etal-2022-database

A Database for Modal Semantic Typology

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.6/

Guo, Qingxia and Imel, Nathaniel and Steinert-Threlkeld, Shane

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

42--51

This paper introduces a database for crosslinguistic modal semantics. The purpose of this database is to (1) enable ongoing consolidation of modal semantic typological knowledge into a repository according to uniform data standards and to (2) provide data for investigations in crosslinguistic modal semantic theory and experiments explaining such theories. We describe the kind of semantic variation that the database aims to record, the format of the data, and a current snapshot of the database, emphasizing access and contribution to the database in light of the goals above. We release the database at \url{https://clmbr.shane.st/modal-typology}.

null

10.18653/v1/2022.sigtyp-1.6

null

22,767

inproceedings

list-etal-2022-sigtyp

The {SIGTYP} 2022 Shared Task on the Prediction of Cognate Reflexes

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.7/

List, Johann-Mattis and Vylomova, Ekaterina and Forkel, Robert and Hill, Nathan and Cotterell, Ryan

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

52--62

This study describes the structure and the results of the SIGTYP 2022 shared task on the prediction of cognate reflexes from multilingual wordlists. We asked participants to submit systems that would predict words in individual languages with the help of cognate words from related languages. Training and surprise data were based on standardized multilingual wordlists from several language families. Four teams submitted a total of eight systems, including both neural and non-neural systems, as well as systems adjusted to the task and systems using more general settings. While all systems showed a rather promising performance, reflecting the overwhelming regularity of sound change, the best performance throughout was achieved by a system based on convolutional networks originally designed for image restoration.

null

10.18653/v1/2022.sigtyp-1.7

null

22,768

inproceedings

jager-2022-bayesian

{B}ayesian Phylogenetic Cognate Prediction

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.8/

J{\"ager, Gerhard

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

63--69

In J{\"ager (2019) a computational framework was defined to start from parallel word lists of related languages and infer the corresponding vocabulary of the shared proto-language. The SIGTYP 2022 Shared Task is closely related. The main difference is that what is to be reconstructed is not the proto-form but an unknown word from an extant language. The system described here is a re-implementation of the tools used in the mentioned paper, adapted to the current task.

null

10.18653/v1/2022.sigtyp-1.8

null

22,769

inproceedings

kirov-etal-2022-mockingbird

Mockingbird at the {SIGTYP} 2022 Shared Task: Two Types of Models for the Prediction of Cognate Reflexes

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.9/

Kirov, Christo and Sproat, Richard and Gutkin, Alexander

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

70--79

The SIGTYP 2022 shared task concerns the problem of word reflex generation in a target language, given cognate words from a subset of related languages. We present two systems to tackle this problem, covering two very different modeling approaches. The first model extends transformer-based encoder-decoder sequence-to-sequence modeling, by encoding all available input cognates in parallel, and having the decoder attend to the resulting joint representation during inference. The second approach takes inspiration from the field of image restoration, where models are tasked with recovering pixels in an image that have been masked out. For reflex generation, the missing reflexes are treated as {\textquotedblleft}masked pixels{\textquotedblright} in an {\textquotedblleft}image{\textquotedblright} which is a representation of an entire cognate set across a language family. As in the image restoration case, cognate restoration is performed with a convolutional network.

null

10.18653/v1/2022.sigtyp-1.9

null

22,770

inproceedings

celano-2022-transformer

A Transformer Architecture for the Prediction of Cognate Reflexes

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.10/

Celano, Giuseppe G. A.

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

80--85

This paper presents the transformer model built to participate in the SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes. It consists of an encoder-decoder architecture with multi-head attention mechanism. Its output is concatenated with the one hot encoding of the language label of an input character sequence to predict a target character sequence. The results show that the transformer outperforms the baseline rule-based system only partially.

null

10.18653/v1/2022.sigtyp-1.10

null

22,771

inproceedings

tresoldi-2022-approaching

Approaching Reflex Predictions as a Classification Problem Using Extended Phonological Alignments

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.11/

Tresoldi, Tiago

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

86--93

This work describes an implementation of the {\textquotedblleft}extended alignment{\textquotedblright} model for cognate reflex prediction submitted to the {\textquotedblleft}SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes{\textquotedblright}. Similarly to List et al. (2022a), the technique involves an automatic extension of sequence alignments with multilayered vectors that encode informational tiers on both site-specific traits, such as sound classes and distinctive features, as well as contextual and suprasegmental ones, conveyed by cross-site referrals and replication. The method allows to generalize the problem of cognate reflex prediction as a classification problem, with models trained using a parallel corpus of cognate sets. A model using random forests is trained and evaluated on the shared task for reflex prediction, and the experimental results are presented and discussed along with some differences to other implementations.

null

10.18653/v1/2022.sigtyp-1.11

null

22,772

inproceedings

chen-etal-2022-investigating

Investigating Information-Theoretic Properties of the Typology of Spatial Demonstratives

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.12/

Chen, Sihan and Futrell, Richard and Mahowald, Kyle

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

94--95

Using data from Nintemann et al. (2020), we explore the variability in complexity and informativity across spatial demonstrative systems using spatial deictic lexicons from 223 languages. We argue from an information-theoretic perspective (Shannon, 1948) that spatial deictic lexicons are efficient in communication, balancing informativity and complexity. Specifically, we find that under an appropriate choice of cost function and need probability over meanings, among all the 21146 theoretically possible spatial deictic lexicons, those adopted by real languages lie near an efficient frontier. Moreover, we find that the conditions that the need probability and the cost function need to satisfy are consistent with the cognitive science literature regarding the source-goal asymmetry. We also show that the data are better explained by introducing a notion of systematicity, which is not currently accounted for in Information Bottleneck approaches to linguistic efficiency.

null

10.18653/v1/2022.sigtyp-1.12

null

22,773

inproceedings

khishigsuren-etal-2022-universal

How Universal is Metonymy? Results from a Large-Scale Multilingual Analysis

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.13/

Khishigsuren, Temuulen and Bella, G{\'a}bor and Brochhagen, Thomas and Marav, Daariimaa and Giunchiglia, Fausto and Batsuren, Khuyagbaatar

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

96--98

Metonymy is regarded by most linguists as a universal cognitive phenomenon, especially since the emergence of the theory of conceptual mappings. However, the field data backing up claims of universality has not been large enough so far to provide conclusive evidence. We introduce a large-scale analysis of metonymy based on a lexical corpus of over 20 thousand metonymy instances from 189 languages and 69 genera. No prior study, to our knowledge, is based on linguistic coverage as broad as ours. Drawing on corpus analysis, evidence of universality is found at three levels: systematic metonymy in general, particular metonymy patterns, and specific metonymy concepts.

null

10.18653/v1/2022.sigtyp-1.13

null

22,774

inproceedings

zanchi-etal-2022-paveda

{P}a{V}e{D}a - {P}avia Verbs Database: Challenges and Perspectives

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.14/

Zanchi, Chiara and Luraghi, Silvia and Combei, Claudia Roberta

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

99--102

This paper describes an ongoing endeavor to construct Pavia Verbs Database (PaVeDa) {--} an open-access typological resource that builds upon previous work on verb argument structure, in particular the Valency Patterns Leipzig (ValPaL) project (Hartmann et al., 2013). The PaVeDa database features four major innovations as compared to the ValPaL database: (i) it includes data from ancient languages enabling diachronic research; (ii) it expands the language sample to language families that are not represented in the ValPaL; (iii) it is linked to external corpora that are used as sources of usage-based examples of stored patterns; (iv) it introduces a new cross-linguistic layer of annotation for valency patterns which allows for contrastive data visualization.

null

10.18653/v1/2022.sigtyp-1.14

null

22,775

inproceedings

saleva-lignos-2022-paranames

{P}ara{N}ames: A Massively Multilingual Entity Name Corpus

Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigtyp-1.15/

S{\"alev{\"a, Jonne and Lignos, Constantine

Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

103--105

We present ParaNames, a Wikidata-derived multilingual parallel name resource consisting of names for approximately 14 million entities spanning over 400 languages. ParaNames is useful for multilingual language processing, both in defining tasks for name translation tasks and as supplementary data for other tasks. We demonstrate an application of ParaNames by training a multilingual model for canonical name translation to and from English.

null

10.18653/v1/2022.sigtyp-1.15

null

22,776

inproceedings

bejarano-etal-2022-perusil

{P}eru{SIL}: A Framework to Build a Continuous {P}eruvian {S}ign {L}anguage Interpretation Dataset

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.1/

Bejarano, Gissella and Huamani-Malca, Joe and Cerna-Herrera, Francisco and Alva-Manchego, Fernando and Rivas, Pablo

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

1--8

Video-based datasets for Continuous Sign Language are scarce due to the challenging task of recording videos from native signers and the reduced number of people who can annotate sign language. COVID-19 has evidenced the key role of sign language interpreters in delivering nationwide health messages to deaf communities. In this paper, we present a framework for creating a multi-modal sign language interpretation dataset based on videos and we use it to create the first dataset for Peruvian Sign Language (LSP) interpretation annotated by hearing volunteers who have intermediate knowledge of PSL guided by the video audio. We rely on hearing people to produce a first version of the annotations, which should be reviewed by native signers in the future. Our contributions: i) we design a framework to annotate a sign Language dataset; ii) we release the first annotated LSP multi-modal interpretation dataset (AEC); iii) we evaluate the annotation done by hearing people by training a sign language recognition model. Our model reaches up to 80.3{\%} of accuracy among a minimum of five classes (signs) AEC dataset, and 52.4{\%} in a second dataset. Nevertheless, analysis by subject in the second dataset show variations worth to discuss.

null

22,778

inproceedings

bigeard-etal-2022-introducing

Introducing Sign Languages to a Multilingual {W}ordnet: Bootstrapping Corpora and Lexical Resources of {G}reek {S}ign {L}anguage and {G}erman {S}ign {L}anguage

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.2/

Bigeard, Sam and Schulder, Marc and Kopf, Maria and Hanke, Thomas and Vasilaki, Kyriaki and Vacalopoulou, Anna and Goulas, Theodore and Dimou, Athanasia-Lida and Fotinea, Stavroula-Evita and Efthimiou, Eleni

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

9--15

Wordnets have been a popular lexical resource type for many years. Their sense-based representation of lexical items and numerous relation structures have been used for a variety of computational and linguistic applications. The inclusion of different wordnets into multilingual wordnet networks has further extended their use into the realm of cross-lingual research. Wordnets have been released for many spoken languages. Research has also been carried out into the creation of wordnets for several sign languages, but none have yet resulted in publicly available datasets. This article presents our own efforts towards an inclusion of sign languages in a multilingual wordnet, starting with Greek Sign Language (GSL) and German Sign Language (DGS). Based on differences in available language resources between GSL and DGS, we trial two workflows with different coverage priorities. We also explore how synergies between both workflows can be leveraged and how future work on additional sign languages could profit from building on existing sign language wordnet data. The results of our work are made publicly available.

null

22,779

inproceedings

borstell-2022-introducing

Introducing the signgloss{R} Package

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.3/

B{\"orstell, Carl

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

16--23

The signglossR package is a library written in the programming language R, intended as an easy-to-use resource for those who work with signed language data and are familiar with R. The package contains a variety of functions designed specifically towards signed language research, facilitating a single-pipeline workflow with R when accessing public language resources remotely (online) or a user`s own files and data. The package specifically targets processing of image and video files, but also features some interaction with software commonly used by researchers working on signed language and gesture, such as ELAN and OpenPose. The signglossR package combines features and functionality from many other libraries and tools in order to simplify and collect existing resources in one place, as well as adding some new functionality, and adapt everything to the needs of researchers working with visual language data. In this paper, the main features of this package are introduced.

null

22,780

inproceedings

brosens-etal-2022-moving

Moving towards a Functional Approach in the {F}lemish {S}ign {L}anguage Dictionary Making Process

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.4/

Brosens, Caro and Janssens, Margot and Verstraete, Sam and Vandamme, Thijs and De Durpel, Hannes

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

24--28

This presentation will outline the dictionary making process of the new online Flemish Sign Language dictionary launched in 2019. First some necessary background information is provided, consisting of a brief history of Flemish Sign Language (VGT) lexicography. Then three phases in the development of the renewed dictionary of VGT will be explored: (i) user research, (ii) data-cleaning and modeling, and (iii) innovations. More than wanting to project a report of lexicographic research on a website, the goal was to make the new dictionary a practical, user-friendly reference tool that meets the needs, expectations, and skills of the dictionary users. To gain a better understanding of who the users were, several sources were consulted: the user research by Joni Oyserman (2013), the quantitative data from Google Analytics and VGTC`s own user profiles. Since 2017, VGTC has been using Signbank, an electronic database specifically developed to compile and manage lexicographic data for sign languages. Bringing together all this raw data inadvertently led to inconsistencies and small mistakes, therefore the data had to be manually revised and complemented. The VGT dictionary was mainly formally modernized, but there are also several substantive differences regarding the previous dictionary: for instance, search options were expanded, and semantic categories were added as well as a new feedback feature. In addition, the new website is also structurally different, it is now responsive to all screen sizes. Lastly, possible future innovations will briefly be discussed. VGTC aims to continuously improve both the user-based interface and the content of the current dictionary. Future goals include, but are not limited to, adding definitions and sample sentences (preferably extracted from the corpus), as well as information on the etymology and common use of signs.

null

22,781

inproceedings

chizhikova-kimmelman-2022-phonetics

Phonetics of Negative Headshake in {R}ussian {S}ign {L}anguage: A Small-Scale Corpus Study

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.5/

Chizhikova, Anastasia and Kimmelman, Vadim

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

29--36

We analyzed negative headshake found in the online corpus of Russian Sign Language. We found that negative headshake can co-occur with negative manual signs, although most of these signs are not accompanied by it. We applied OpenFace, a Computer Vision toolkit, to extract head rotation measurements from video recordings, and analyzed the headshake in terms of the number of peaks (turns), the amplitude of the turns, and their frequency. We find that such basic phonetic measurements of headshake can be extracted using a combination of manual annotation and Computer Vision, and can be further used in comparative research across constructions and sign languages.

null

22,782

inproceedings

choubsaz-etal-2022-documenting

Documenting the Use of {I}ranian Sign Language ({ZEI}) in Kermanshah

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.6/

Choubsaz, Yassaman and Crasborn, Onno and Siyavoshi, Sara and Soleimanbeigi, Farzaneh

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

37--41

We describe a sign language documentation project funded by the Endangered Languages Documentation Project (ELDP) in the province of Kermanshah, a city in west of Iran. The deposit at ELDP archive (elararchive.org) includes recording of 38 native signers of Zaban Eshareh Irani living in Kermanshah. The recordings start with an elicitation of the signs of the Farsi alphabet along with fingerspelling of some words as well as vocabulary elicitation of some basic concepts. Subsequently, the participants are asked to watch short movies and then they are asked to retell the story. Later, the participants have natural conversations in pairs guided by a deaf moderator. Initial annotations of ID-glosses and translations to Persian and English were also archived. ID-glosses are stored as a dataset in Global Signbank, along with a citation form of signs and their phonological description. The resulting datasets and one-hour annotation of the conversations are available to other researchers in ELDP archive.

null

22,783

inproceedings

danet-etal-2022-applying

Applying the Transcription System Typannot to Mouth Gestures

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.7/

Danet, Claire and Thomas, Chlo{\'e} and Contesse, Adrien and R{\'e}bulard, Morgane and Bianchini, Claudia S. and Chevrefils, L{\'e}a and Doan, Patrick

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

42--47

Research on sign languages (SLs) requires dedicated, efficient and comprehensive transcription systems to analyze and compare the sign parameters; at present, many transcription systems focus on manual parameters, relegating the non-manual component to a lesser role. This article presents Typannot, a formal transcription system, and in particular its application to mouth gestures: 1) first, exposing its kinesiological approach, i.e. an intrinsic articulatory description anchored in the body; 2) then, showing its conception to integrate linguistic, graphic and technical aspects within a typeface; 3) finally, presenting its application to a corpus in French Sign Language (LSF) recorded with motion capture.

null

22,784

inproceedings

de-quadros-etal-2022-libras

{L}ibras Portal: A Way of Documentation, a Way of Sharing

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.8/

de Quadros, Ronice and Krusser, Renata and Saito, Daniela

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

48--52

Libras Portal is an interface that makes available in one single site a series of elements and tools related to the Brazilian Sign Language (Libras) and comprises Libras documentation which may be employed for research and for educational aims. Libras Portal was developed to codify tools that prop an education network and practice community, making possible the sharing of knowledge, data, and interaction in Libras and Portuguese. It involves accessibility and usability of the web, especially videos in Libras. The latter are access-friendly to available hyperlinks and tools related to communication with the target practice community. The layout also employs visual and textual resources for deaf users. The portal makes available resources for research and the teaching of language, namely Libras Grammar, Libras corpus, Sign Bank, and Literary Anthology of Libras. It is also a store for the sharing of literary, academic, and didactic materials, courses, glossaries, anthologies, lesson models, and grammar analyses. Consequently, tools were developed for the accessibility of deaf people, for easy web browsing, index information, video upload, research, and development of products for communities of deaf people. The current paper will describe the development of research and resources for accessibility.

null

22,785

inproceedings

filhol-mcdonald-2022-representation

Representation and Synthesis of Geometric Relocations

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.9/

Filhol, Michael and McDonald, John

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

53--58

One of the key features of signed discourse is the geometric placements of gestural units in signing space. Signers use the geometry of signing space to describe the placements and forms of objects and also use it to contrast participants or locales in a story. Depending on the specific functions of the placement in the discourse, features such as geometric precision, gaze redirection and timing will all differ. A signing avatar must capture these differences to sign such discourse naturally. This paper builds on prior work that animated geometric depictions to enable a signing avatar to more naturally use signing space for opposing participants and concepts in discourse. Building from a structured linguistic description of a signed newscast, they system automatically synthesizes animation that correctly utilizes signing space to lay out the opposing locales in the report. The efficacy of the approach is demonstrated through comparisons of the avatar`s motion with the source signing.

null

22,786

inproceedings

hall-etal-2022-sign

Sign Language Phonetic Annotator-Analyzer: Open-Source Software for Form-Based Analysis of Sign Languages

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.10/

Hall, Kathleen Currie and Aonuki, Yurika and Vesik, Kaili and Poy, April and Tolmie, Nico

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

59--66

This paper provides an introduction to the Sign Language Phonetic Annotator-Analyzer (SLP-AA) software, a free and open-source tool currently under development, for facilitating detailed form-based transcription of signs. The software is designed to have a user-friendly interface that allows coders to transcribe a great deal of phonetic detail without being constrained to a particular phonetic annotation system or phonological framework. Here, we focus on the {\textquoteleft}annotator' component of the software, outlining the functionality for transcribing movement, location, hand configuration, orientation, and contact, as well as the timing relations between them.

null

22,787

inproceedings

hassan-etal-2022-asl

{ASL}-Homework-{RGBD} Dataset: An Annotated Dataset of 45 Fluent and Non-fluent Signers Performing {A}merican {S}ign {L}anguage Homeworks

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.11/

Hassan, Saad and Seita, Matthew and Berke, Larwan and Tian, Yingli and Gale, Elaine and Lee, Sooyeon and Huenerfauth, Matt

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

67--72

We are releasing a dataset containing videos of both fluent and non-fluent signers using American Sign Language (ASL), which were collected using a Kinect v2 sensor. This dataset was collected as a part of a project to develop and evaluate computer vision algorithms to support new technologies for automatic detection of ASL fluency attributes. A total of 45 fluent and non-fluent participants were asked to perform signing homework assignments that are similar to the assignments used in introductory or intermediate level ASL courses. The data is annotated to identify several aspects of signing including grammatical features and non-manual markers. Sign language recognition is currently very data-driven and this dataset can support the design of recognition technologies, especially technologies that can benefit ASL learners. This dataset might also be interesting to ASL education researchers who want to contrast fluent and non-fluent signing.

null

22,788

inproceedings

isard-konrad-2022-dgs

{MY} {DGS} {--} {ANNIS}: {ANNIS} and the {P}ublic {DGS} {C}orpus

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.12/

Isard, Amy and Konrad, Reiner

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

73--79

In 2018 the DGS-Korpus project published the first full release of the Public DGS Corpus. The data have already been published in two different ways to fulfil the needs of different user groups, and we have now published the third portal MY DGS {--} ANNIS using the ANNIS browser-based corpus software. ANNIS is a corpus query tool for visualization and querying of multi-layer corpus data. It has its own query language, AQL, and is accessed from a web browser without requiring a login. It allows more complex queries and visualizations than those provided by the existing research portal. We introduce ANNIS and its query language AQL, describe the structure of MY DGS {--} ANNIS, and give some example queries. The use cases with queries over multiple annotation tiers and metadata illustrate the research potential of this powerful tool and show how students and researchers can explore the Public DGS Corpus.

null

22,789

inproceedings

jahn-etal-2022-outreach

Outreach and Science Communication in the {DGS}-Korpus Project: Accessibility of Data and the Benefit of Interactive Exchange between Communities

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.13/

Jahn, Elena and Khan, Calvin and Herrmann, Annika

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

80--87

In this paper, we tackle the issues of science communication and dissemination within a sign language corpus project with a focus on spreading accessible information and involving the D/deaf community on various levels. We will discuss successful examples, challenges, and limitations to public relations in such a project and particularly elaborate on use cases. The focus group is presented as a best-practice example of a what we think is a necessary perspective: taking external knowledge seriously and let community experts interact with and provide feedback on a par with academic personnel. Showing both social media and on-site events, we present some exemplary approaches from our team involved in public relations. Keywords: public relations, science communication, sign language community, DGS-Korpus project

null

22,790

inproceedings

jedlicka-etal-2022-mc

{MC}-{TRISLAN}: A Large 3{D} Motion Capture Sign Language Data-set

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.14/

Jedli{\v{c}}ka, Pavel and Kr{\v{n}}oul, Zden{\v{e}}k and Zelezny, Milos and Muller, Ludek

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

88--93

The new 3D motion capture data corpus expands the portfolio of existing language resources by a corpus of 18 hours of Czech sign language. This helps to alleviate the current problem, which is a critical lack of high quality data necessary for research and subsequent deployment of machine learning techniques in this area. We currently provide the largest collection of annotated sign language recordings acquired by state-of-the-art 3D human body recording technology for the successful future deployment in communication technologies, especially machine translation and sign language synthesis.

null

22,791

inproceedings

jui-etal-2022-machine

A Machine Learning-based Segmentation Approach for Measuring Similarity between Sign Languages

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.15/

Jui, Tonni Das and Bejarano, Gissella and Rivas, Pablo

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

94--101

Due to the lack of more variate, native and continuous datasets, sign languages are low-resources languages that can benefit from multilingualism in machine translation. In order to analyze the benefits of approaches like multilingualism, finding the similarity between sign languages can guide better matches and contributions between languages. However, calculating the similarity between sign languages again implies a laborious work to measure how close or distant signs are and their respective contexts. For that reason, we propose to support the similarity measurement between sign languages through a video-segmentation-based machine learning model that will quantify this match among signs of different countries' sign languages. Using a machine learning approach the similarity measurement process can run more smoothly, compared to a more manual approach. We use a pre-trained temporal segmentation model for British Sign Language (BSL). We test it on three datasets, an American Sign Language (ASL) dataset, an Indian Sign Language (ISL), and an Australian Sign Language (AUSLAN) dataset. We hypothesize that the percentage of segmented and recognized signs by this machine learning model can represent the percentage of overlap or similarity between British and the other three sign languages. In our ongoing work, we evaluate three metrics considering Swadesh`s and Woodward`s list and their synonyms. We found that our intermediate-strict metric coincides with a more classical analysis of the similarity between British and American Sign Language, as well as with the classical low measurement between Indian and British sign languages. On the other hand, our similarity measurement between British and Australian Sign language just holds for part of the Australian Sign Language and not the whole data sample.

null

22,792

inproceedings

kopf-etal-2022-sign

The Sign Language Dataset Compendium: Creating an Overview of Digital Linguistic Resources

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.16/

Kopf, Maria and Schulder, Marc and Hanke, Thomas

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

102--109

One of the challenges that sign language researchers face is the identification of suitable language datasets, particularly for cross-lingual studies. There is no single source of information on what sign language corpora and lexical resources exist or how they compare. Instead, they have to be found through extensive literature review or word-of-mouth. The amount of information available on individual datasets can also vary widely and may be distributed across different publications, data repositories and (potentially defunct) project websites. This article introduces the Sign Language Dataset Compendium, an extensive overview of linguistic resources for sign languages. It covers existing corpora and lexical resources, as well as commonly used data collection tasks. Special attention is paid to covering resources for many different languages from around the globe. All information is provided in a standardised format to make entries comparable, but kept flexible enough to allow for differences in content. The compendium is intended as a growing resource that will be updated regularly.

null

22,793

inproceedings

kuder-2022-making

Making Sign Language Corpora Comparable: A Study of Palm-Up and Throw-Away in {P}olish {S}ign {L}anguage, {G}erman {S}ign {L}anguage, and {R}ussian {S}ign {L}anguage

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.17/

Kuder, Anna

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

110--117

This paper is primarily devoted to describing the preparation phase of a large-scale comparative study based on naturalistic linguistic data drawn from multiple sign language corpora. To provide an example, I am using my current project on manual gestural elements in Polish Sign Language, German Sign Language, and Russian Sign Language. The paper starts with a description of the reasons behind undertaking this project. Then, I describe the scope of my study, which is focused on two manual elements present in all three mentioned sign languages: palm-up and throw-away; and the three corpora which are my data sources. This is followed by a presentation of the steps taken in the initial stages of the project in order to make the data comparable. Those steps are: choosing the adequate data samples from all three corpora, gathering all data within the chosen software, and creating an annotation schema that builds on the annotations already present in all three corpora. Even though the project is still underway, and the annotation process is ongoing, preliminary discussions about the nature of the analysed manual activities are presented based on the initial annotations for the sake of evaluating the created annotation schema. I conclude the paper with some remarks about the performance of the employed methodology.

null

22,794

inproceedings

kuder-etal-2022-open

Open Repository of the {P}olish {S}ign {L}anguage Corpus: Publication Project of the {P}olish {S}ign {L}anguage Corpus

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.18/

Kuder, Anna and W{\'o}jcicka, Joanna and Mostowski, Piotr and Rutkowski, Pawe{\l}

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

118--123

Between 2010 and 2020, the research team of the Section for Sign Linguistics collected, annotated, and translated a large corpus of Polish Sign Language (polski j{\k{e}}zyk migowy, PJM). After this task was finished, a substantial part of the gathered materials was published online as the Open Repository of the Polish Sign Language Corpus. The current paper gives an overview of the process of converting the material from the Corpus into the Repository. If presents and explains the decisions made along the way and describes the process of data preparation and publication. There are two levels of access to the Repository, which are meant to fulfil the needs of a wide range of public users, from members of the Deaf community, through hearing students of PJM, sign language teachers and interpreters, to users with academic background. We describe how corpus material available in open access was prepared to be searchable by text type and elicitation tasks, by sociolinguistic metadata, and by translation into written Polish. We go on to explain how access for research purposes differs from open access. We present possible ways in which data gathered in the Repository may be used by members of the signing community in Poland and abroad.

null

22,795

inproceedings

kuznetsova-etal-2022-functional

Functional Data Analysis of Non-manual Marking of Questions in {K}azakh-{R}ussian {S}ign {L}anguage

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.19/

Kuznetsova, Anna and Imashev, Alfarabi and Mukushev, Medet and Sandygulova, Anara and Kimmelman, Vadim

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

124--131

This paper is a continuation of Kuznetsova et al. (2021), which described non-manual markers of polar and wh-questions in comparison with statements in an NLP dataset of Kazakh-Russian Sign Language (KRSL) using Computer Vision. One of the limitations of the previous work was the distortion of the 3D face landmarks when the head was rotated. The proposed solution was to train a simple linear regression model to predict the distortion and then subtract it from the original output. We improve this technique with a multilayer perceptron. Another limitation that we intend to address in this paper is the discrete analysis of the continuous movement of non-manuals. In Kuznetsova et al. (2021) we averaged the value of the non-manual over its scope for statistical analysis. To preserve information on the shape of the movement, in this study we use a statistical tool that is often used in speech research, Functional Data Analysis, specifically Functional PCA.

null

22,796

inproceedings

martinod-etal-2022-two

Two New {AZ}ee Production Rules Refining Multiplicity in {F}rench {S}ign {L}anguage

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.20/

Martinod, Emmanuella and Danet, Claire and Filhol, Michael

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

132--138

This paper is a contribution to sign language (SL) modeling. We focus on the hitherto imprecise notion of {\textquotedblleft}Multiplicity{\textquotedblright}, assumed to express plurality in French Sign Language (LSF), using AZee approach. AZee is a linguistic and formal approach to modeling LSF. It takes into account the linguistic properties and specificities of LSF while respecting constraints linked to a modeling process. We present the methodology to extract AZee production rules. Based on the analysis of strong form-meaning associations in SL data (elicited image descriptions and short news), we identified two production rules structuring the expression of multiplicity in LSF. We explain how these newly extracted production rules are different from existing ones. Our goal is to refine the AZee approach to allow the coverage of a growing part of LSF. This work could lead to an improvement in SL synthesis and SL automatic translation.

null

22,797

inproceedings

moiselle-leeson-2022-language

Language Planning in Action: Depiction as a Driver of New Terminology in {I}rish {S}ign {L}anguage

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.21/

Moiselle, Rachel and Leeson, Lorraine

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

139--143

In this paper, we examine the linguistic phenomenon known as {\textquoteleft}depiction', which relates to the ability to visually represent semantic components (Dudis, 2004). While some elements of this have been described for Irish Sign Language, with particular attention to the {\textquoteleft}productive lexicon' (Leeson {\&} Grehan, 2004; Leeson {\&} Saeed, 2012; Matthews, 1996; O`Baoill {\&} Matthews, 2000), here, we take the analysis further, drawing on what we have learned from cognitive linguistics over the past decade. Drawing on several recently developed domain-specific glossaries (e.g., STEM1, Covid-192, political domain, Sexual, Domestic and Gender Based Violence (SDGBV)-related vocabulary) we present ongoing analysis indicating that a deliberate focus on iconicity, in particular, elements of depiction, appears to be a primary driver. We also consider the potential implications of the insights we intend to gain from Deaf-led glossary glossary development work in the context of Machine Translation goals, for example, for work in progress on the Horizon 2020 funded SignON project.

null

22,798

inproceedings

morgan-etal-2022-facilitating

Facilitating the Spread of New Sign Language Technologies across {E}urope

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.22/

Morgan, Hope and Crasborn, Onno and Kopf, Maria and Schulder, Marc and Hanke, Thomas

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

144--147

For developing sign language technologies like automatic translation, huge amounts of training data are required. Even the larger corpora available for some sign languages are tiny compared to the amounts of data used for corresponding spoken language technologies. The overarching goal of the European project EASIER is to develop a framework for bidirectional automatic translation between sign and spoken languages and between sign languages. One part of this multi-dimensional project is that it will pool available language resources from European sign languages into a larger dataset to address the data scarcity problem. This approach promises to open the floor for lower-resourced sign languages in Europe. This article focusses on efforts in the EASIER project to allow for new languages to make use of such technologies in the future. What are the characteristics of sign language resources needed to train recognition, translation, and synthesis algorithms, and how can other countries including those without any sign resources follow along with these developments? The efforts undertaken in EASIER include creating workflow documents and organizing training sessions in online workshops. They reflect the current state of the art, and will likely need to be updated in the coming decade.

null

22,799

inproceedings

morgan-etal-2022-isl

{ISL}-{LEX} v.1: An Online Lexical Resource of {I}sraeli {S}ign {L}anguage

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.23/

Morgan, Hope and Sandler, Wendy and Stamp, Rose and Novogrodsky, Rama

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

148--153

This paper describes a new online lexical resource and interactive tool for Israeli Sign Language, ISL-LEX v.1. The dataset contains 961 non-compound ISL signs with the following information: subjective frequency ratings from native signers, iconicity ratings from native and non-native signers (presented separately), and phonological properties in six domains. The selection of signs was also designed to reflect a broad distinction between those signs acquired early in childhood and those acquired later. ISL-LEX is an online interface built using the SIGN-LEX visualization (Caselli et al. 2022), and is intended for use by researchers, educators, and students. It is therefore offered in two text-based versions, English and Hebrew, with video instructions in ISL.

null

22,800

inproceedings

mukushev-etal-2022-towards

Towards Large Vocabulary {K}azakh-{R}ussian {S}ign {L}anguage Dataset: {KRSL}-{O}nline{S}chool

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.24/

Mukushev, Medet and Kydyrbekova, Aigerim and Kimmelman, Vadim and Sandygulova, Anara

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

154--158

This paper presents a new dataset for Kazakh-Russian Sign Language (KRSL) created for the purposes of Sign Language Processing. In 2020, Kazakhstan`s schools were quickly switched to online mode due to the COVID-19 pandemic. Every working day, the El-arna TV channel was broadcasting video lessons for grades from 1 to 11 with sign language translation. This opportunity allowed us to record a corpus with a large vocabulary and spontaneous SL interpretation. To this end, this corpus contains video recordings of Kazakhstan`s online school translated to Kazakh-Russian sign language by 7 interpreters. At the moment we collected and cleaned 890 hours of video material. A custom annotation tool was created to make the process of data annotation simple and easy-to-use by the Deaf community. To date, around 325 hours of videos have been annotated with glosses and 4,009 lessons out of 4,547 were transcribed with automatic speech-to-text software. The KRSL-OnlineSchool dataset will be made publicly available at \url{https://krslproject.github.io/online-school/}

null

22,801

inproceedings

mukushev-etal-2022-towards-semi

Towards Semi-automatic Sign Language Annotation Tool: {SLAN}-tool

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.25/

Mukushev, Medet and Sabyrov, Arman and Sultanova, Madina and Kimmelman, Vadim and Sandygulova, Anara

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

159--164

This paper presents a semi-automatic annotation tool for sign languages namely SLAN-tool. The SLAN-tool provides a web-based service for the annotation of sign language videos. Researchers can use the SLAN-tool web service to annotate new and existing sign language datasets with different types of annotations, such as gloss, handshape configurations, and signing regions. This is allowed using a custom tier adding functionality. A unique feature of the tool is its automatic annotation functionality which uses several neural network models in order to recognize signing segments from videos and classify handshapes according to HamNoSys handshape inventory. Furthermore, SLAN-tool users can export annotations and import them into ELAN. The SLAN-tool is publicly available at \url{https://slan-tool.com}.

null

22,802

inproceedings

neidle-etal-2022-resources

Resources for Computer-Based Sign Recognition from Video, and the Criticality of Consistency of Gloss Labeling across Multiple Large {ASL} Video Corpora

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.26/

Neidle, Carol and Opoku, Augustine and Ballard, Carey and Dafnis, Konstantinos M. and Chroni, Evgenia and Metaxas, Dimitri

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

165--172

The WLASL purports to be {\textquotedblleft}the largest video dataset for Word-Level American Sign Language (ASL) recognition.{\textquotedblright} It brings together various publicly shared video collections that could be quite valuable for sign recognition research, and it has been used extensively for such research. However, a critical problem with the accompanying annotations has heretofore not been recognized by the authors, nor by those who have exploited these data: There is no 1-1 correspondence between sign productions and gloss labels. Here we describe a large (and recently expanded and enhanced), linguistically annotated, downloadable, video corpus of citation-form ASL signs shared by the American Sign Language Linguistic Research Project (ASLLRP){---}with 23,452 sign tokens and an online Sign Bank{---}in which such correspondences are enforced. We furthermore provide annotations for 19,672 of the WLASL video examples consistent with ASLLRP glossing conventions. For those wishing to use WLASL videos, this provides a set of annotations that makes it possible: (1) to use those data reliably for computational research; and/or (2) to combine the WLASL and ASLLRP datasets, creating a combined resource that is larger and richer than either of those datasets individually, with consistent gloss labeling for all signs. We also offer a summary of our own sign recognition research to date that exploits these data resources.

null

22,803

inproceedings

power-etal-2022-signed

Signed Language Transcription and the Creation of a Cross-linguistic Comparative Database

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.27/

Power, Justin and Quinto-Pozos, David and Law, Danny

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

173--180

As the availability of signed language data has rapidly increased, sign scholars have been confronted with the challenge of creating a common framework for the cross-linguistic comparison of the phonological forms of signs. While transcription techniques have played a fundamental role in the creation of cross-linguistic comparative databases for spoken languages, transcription has featured much less prominently in sign research and lexicography. Here we report the experiences of the Sign Change project in using the signed language transcription system HamNoSys to create a comparative database of basic vocabulary for thirteen signed languages. We report the results of a small-scale study, in which we measured (i) the average time required for two trained transcribers to complete a transcription and (ii) the similarity of their independently produced transcriptions. We find that, across the two transcribers, the transcription of one sign required, on average, one minute and a half. We also find that the similarity of transcriptions differed across phonological parameters. We consider the implications of our findings about transcription time and transcription similarity for other projects that plan to incorporate transcription techniques.

null

22,804

inproceedings

smith-etal-2022-integrating

Integrating {A}uslan Resources into the Language Data Commons of {A}ustralia

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.28/

Smith, River Tae and Willoughby, Louisa and Johnston, Trevor

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

181--186

This paper describes a project to secure Auslan (Australian Sign Language) resources within a national language data network called the Language Data Commons of Australia (LDaCA). The resources are Auslan Signbank, a web-based multi-media dictionary, and the Auslan Corpus, a collection of video recordings of the language being used in various contexts with time-aligned ELAN annotation files. We aim to make these resources accessible to the language community, encourage community participation in the curation of the data, and facilitate and extend their uses in language teaching and linguistic research. The software platforms of both resources will be made compatible with other LDaCA resources; and the two will also be aggregated and linked so that (i) users of the dictionary can view attested corpus examples for an entry; and (ii) users of the corpus can instantly view the dictionary entry for an already glossed sign to check phonological, lexical and grammatical information about it, and/or to ensure that the correct annotation gloss (aka {\textquoteleft}ID-gloss') for a sign token has been chosen. This will enhance additions to annotations in the Auslan Corpus, entries in Auslan Signbank and the integrity of research based on both.

null

22,805

inproceedings

stamp-etal-2022-capturing

Capturing Distalization

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.29/

Stamp, Rose and Khatib, Lilyana and Hel-Or, Hagit

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

187--191

Coding and analyzing large amounts of video data is a challenge for sign language researchers, who traditionally code 2D video data manually. In recent years, the implementation of 3D motion capture technology as a means of automatically tracking movement in sign language data has been an important step forward. Several studies show that motion capture technologies can measure sign language movement parameters {--} such as volume, speed, variance {--} with high accuracy and objectivity. In this paper, using motion capture technology and machine learning, we attempt to automatically measure a more complex feature in sign language known as distalization. In general, distalized signs use the joints further from the torso (such as the wrist), however, the measure is relative and therefore distalization is not straightforward to measure. The development of a reliable and automatic measure of distalization using motion tracking technology is of special interest in many fields of sign language research.

null

22,806

inproceedings

stamp-etal-2022-corpus

The Corpus of {I}sraeli {S}ign {L}anguage

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.30/

Stamp, Rose and Ohanin, Ora and Lanesman, Sara

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

192--197

The Corpus of Israeli Sign Language is a four-year project (2020-2024) which aims to create a digital open-access corpus of spontaneous and elicited data from a representative sample of the Israeli deaf community. In this paper, the methodology for building the Corpus of Israeli Sign Language is described. Israeli Sign Language (ISL) is the main sign language used across Israel by around 10,000 people. As part of the corpus, data will be collected from 120 deaf ISL signers across four sites in Israel: Tel Aviv and the Centre, Haifa and the North, Be`er Sheva and the South and Jerusalem and the surrounding area. Participants will engage in a variety of tasks, eliciting a range of signing styles from free conversation to lexical elicitation. The dataset will consist of recordings of over 360 hours of video data which will be used to conduct sociolinguistic investigations of language contact, variation, and change in the near term, and other linguistic analyses in the future.

null

22,807

inproceedings

woll-etal-2022-segmentation

Segmentation of Signs for Research Purposes: Comparing Humans and Machines

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.31/

Woll, Bencie and Fox, Neil and Cormier, Kearsy

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

198--201

Sign languages such as British Sign Language (BSL) are visual languages which lack standard writing systems. Annotation of sign language data, especially for the purposes of machine readability, is therefore extremely slow. Tools to help automate and thus speed up the annotation process are very much needed. Here we test the development of one such tool (VIA-SLA), which uses temporal convolutional networks (Renz et al., 2021a, b) for the purpose of segmenting continuous signing in any sign language, and is designed to integrate smoothly with ELAN, the widely used annotation software for analysis of videos of sign language. We compare automatic segmentation by machine with segmentation done by a human, both in terms of time needed and accuracy of segmentation, using samples taken from the BSL Corpus (Schembri et al., 2014). A small sample of four short video files is tested (mean duration 25 seconds). We find that mean accuracy in terms of number and location of segmentations is relatively high, at around 78{\%}. This preliminary test suggests that VIA-SLA promises to be very useful for sign linguists.

null

22,808

inproceedings

xia-etal-2022-sign

Sign Language Video Anonymization

Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc

jun

2022

Marseille, France

European Language Resources Association

https://aclanthology.org/2022.signlang-1.32/

Xia, Zhaoyang and Chen, Yuxiao and Zhangli, Qilong and Huenerfauth, Matt and Neidle, Carol and Metaxas, Dimitri

Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

202--211

Deaf signers who wish to communicate in their native language frequently share videos on the Web. However, videos cannot preserve privacy{---}as is often desirable for discussion of sensitive topics{---}since both hands and face convey critical linguistic information and therefore cannot be obscured without degrading communication. Deaf signers have expressed interest in video anonymization that would preserve linguistic content. However, attempts to develop such technology have thus far shown limited success. We are developing a new method for such anonymization, with input from ASL signers. We modify a motion-based image animation model to generate high-resolution videos with the signer identity changed, but with the preservation of linguistically significant motions and facial expressions. An asymmetric encoder-decoder structured image generator is used to generate the high-resolution target frame from the low-resolution source frame based on the optical flow and confidence map. We explicitly guide the model to attain a clear generation of hands and faces by using bounding boxes to improve the loss computation. FID and KID scores are used for the evaluation of the realism of the generated frames. This technology shows great potential for practical applications to benefit deaf signers.

null

22,809

inproceedings

gupta-2022-building

On Building Spoken Language Understanding Systems for Low Resourced Languages

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.1/

Gupta, Akshat

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

1--11

Spoken dialog systems are slowly becoming an integral part of the human experience due to their various advantages over textual interfaces. Spoken language understanding (SLU) systems are fundamental building blocks of spoken dialog systems. But creating SLU systems for low resourced languages is still a challenge. In a large number of low resourced language, we don`t have access to enough data to build automatic speech recognition (ASR) technologies, which are fundamental to any SLU system. Also, ASR based SLU systems do not generalize to unwritten languages. In this paper, we present a series of experiments to explore extremely low-resourced settings where we perform intent classification with systems trained on as low as one data-point per intent and with only one speaker in the dataset. We also work in a low-resourced setting where we do not use language specific ASR systems to transcribe input speech, which compounds the challenge of building SLU systems to simulate a true low-resourced setting. We test our system on Belgian Dutch (Flemish) and English and find that using phonetic transcriptions to make intent classification systems in such low-resourced setting performs significantly better than using speech features. Specifically, when using a phonetic transcription based system over a feature based system, we see average improvements of 12.37{\%} and 13.08{\%} for binary and four-class classification problems respectively, when averaged over 49 different experimental settings.

null

10.18653/v1/2022.sigmorphon-1.1

null

22,811

inproceedings

todd-etal-2022-unsupervised

Unsupervised morphological segmentation in a language with reduplication

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.2/

Todd, Simon and Huang, Annie and Needle, Jeremy and Hay, Jennifer and King, Jeanette

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

12--22

We present an extension of the Morfessor Baseline model of unsupervised morphological segmentation (Creutz and Lagus, 2007) that incorporates abstract templates for reduplication, a typologically common but computationally underaddressed process. Through a detailed investigation that applies the model to Maori, the ̄ Indigenous language of Aotearoa New Zealand, we show that incorporating templates improves Morfessor`s ability to identify instances of reduplication, and does so most when there are multiple minimally-overlapping templates. We present an error analysis that reveals important factors to consider when applying the extended model and suggests useful future directions.

null

10.18653/v1/2022.sigmorphon-1.2

null

22,812

inproceedings

hutin-allassonniere-tang-2022-investigating

Investigating phonological theories with crowd-sourced data: The Inventory Size Hypothesis in the light of Lingua Libre

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.3/

Hutin, Mathilde and Allassonni{\`e}re-Tang, Marc

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

23--28

Data-driven research in phonetics and phonology relies massively on oral resources, and access thereto. We propose to explore a question in comparative linguistics using an open-source crowd-sourced corpus, Lingua Libre, Wikimedia`s participatory linguistic library, to show that such corpora may offer a solution to typologists wishing to explore numerous languages at once. For the present proof of concept, we compare the realizations of Italian and Spanish vowels (sample size = 5000) to investigate whether vowel production is influenced by the size of the phonemic inventory (the Inventory Size Hypothesis), by the exact shape of the inventory (the Vowel Quality Hypothesis) or by none of the above. Results show that the size of the inventory does not seem to influence vowel production, thus supporting previous research, but also that the shape of the inventory may well be a factor determining the extent of variation in vowel production. Most of all, these results show that Lingua Libre has the potential to provide valuable data for linguistic inquiry.

null

10.18653/v1/2022.sigmorphon-1.3

null

22,813

inproceedings

ha-vu-etal-2022-logical

Logical Transductions for the Typology of Ditransitive Prosody

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.4/

Vu, Mai Ha and De Santo, Aniello and Dolatian, Hossep

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

29--38

Given the empirical landscape of possible prosodic parses, this paper examines the computations required to formalize the mapping from syntactic structure to prosodic structure. In particular, we use logical tree transductions to define the prosodic mapping of ditransitive verb phrases in SVO languages, building off of the typology described in Kalivoda (2018). Explicit formalization of syntax-prosody mapping revealed a number of unanswered questions relating to the fine details of theoretical assumptions behind prosodic mapping.

null

10.18653/v1/2022.sigmorphon-1.4

null

22,814

inproceedings

downey-etal-2022-masked

A Masked Segmental Language Model for Unsupervised Natural Language Segmentation

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.5/

Downey, C.m. and Xia, Fei and Levow, Gina-Anne and Steinert-Threlkeld, Shane

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

39--50

We introduce a Masked Segmental Language Model (MSLM) for joint language modeling and unsupervised segmentation. While near-perfect supervised methods have been developed for segmenting human-like linguistic units in resource-rich languages such as Chinese, many of the world`s languages are both morphologically complex, and have no large dataset of {\textquotedblleft}gold{\textquotedblright} segmentations for supervised training. Segmental Language Models offer a unique approach by conducting unsupervised segmentation as the byproduct of a neural language modeling objective. However, current SLMs are limited in their scalability due to their recurrent architecture. We propose a new type of SLM for use in both unsupervised and lightly supervised segmentation tasks. The MSLM is built on a span-masking transformer architecture, harnessing a masked bidirectional modeling context and attention, as well as adding the potential for model scalability. In a series of experiments, our model outperforms the segmentation quality of recurrent SLMs on Chinese, and performs similarly to the recurrent model on English.

null

10.18653/v1/2022.sigmorphon-1.5

null

22,815

inproceedings

dolatian-etal-2022-trees

Trees probe deeper than strings: an argument from allomorphy

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.6/

Dolatian, Hossep and Ikawa, Shiori and Graf, Thomas

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

51--60

Linguists disagree on whether morphological representations should be strings or trees. We argue that tree-based views of morphology can provide new insights into morphological complexity even in cases where the posited tree structure closely matches the surface string. Our argument is based on a subregular case study of morphologically conditioned allomorphy, where the phonological form of some morpheme (the target) is conditioned by the presence of some other morpheme (the trigger) somewhere within the morphosyntactic context. The trigger and target can either be linearly adjacent or non-adjacent, and either the trigger precedes the target (inwardly sensitive) or the target precedes the trigger (outwardly sensitive). When formalized as string transductions, the only complexity difference is between local and non-local allomorphy. Over trees, on the other hand, we also see a complexity difference between inwardly sensitive and outwardly sensitive allomorphy. Just as unboundedness assumptions can sometimes tease apart patterns that are equally complex in the finitely bounded case, tree-based representations can reveal differences that disappear over strings.

null

10.18653/v1/2022.sigmorphon-1.6

null

22,816

inproceedings

bafna-zabokrtsky-2022-subword

Subword-based Cross-lingual Transfer of Embeddings from {H}indi to {M}arathi and {N}epali

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.7/

Bafna, Niyati and {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

61--71

Word embeddings are growing to be a crucial resource in the field of NLP for any language. This work introduces a novel technique for static subword embeddings transfer for Indic languages from a relatively higher resource language to a genealogically related low resource language. We primarily work with HindiMarathi, simulating a low-resource scenario for Marathi, and confirm observed trends on Nepali. We demonstrate the consistent benefits of unsupervised morphemic segmentation on both source and target sides over the treatment performed by fastText. Our best-performing approach uses an EM-style approach to learning bilingual subword embeddings; we also show, for the first time, that a trivial {\textquotedblleft}copyand-paste{\textquotedblright} embeddings transfer based on even perfect bilingual lexicons is inadequate in capturing language-specific relationships. We find that our approach substantially outperforms the fastText baselines for both Marathi and Nepali on the Word Similarity task as well as WordNetBased Synonymy Tests; on the former task, its performance for Marathi is close to that of pretrained fastText embeddings that use three orders of magnitude more Marathi data.

null

10.18653/v1/2022.sigmorphon-1.7

null

22,817

inproceedings

tanner-etal-2022-multidimensional

Multidimensional acoustic variation in vowels across {E}nglish dialects

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.8/

Tanner, James and Sonderegger, Morgan and Stuart-Smith, Jane

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

72--82

Vowels are typically characterized in terms of their static position in formant space, though vowels have also been long-known to undergo dynamic formant change over their timecourse. Recent studies have demonstrated that this change is highly informative for distinguishing vowels within a system, as well as providing additional resolution in characterizing differences between dialects. It remains unclear, however, how both static and dynamic representations capture the main dimensions of vowel variation across a large number of dialects. This study examines the role of static, dynamic, and duration information for 5 vowels across 21 British and North American English dialects, and observes that vowels exhibit highly structured variation across dialects, with dialects displaying similar patterns within a given vowel, broadly corresponding to a spectrum between traditional {\textquoteleft}monophthong' and {\textquoteleft}diphthong' characterizations. These findings highlight the importance of dynamic and duration information in capturing how vowels can systematically vary across a large number of dialects, and provide the first large-scale description of formant dynamics across many dialects of a single language.

null

10.18653/v1/2022.sigmorphon-1.8

null

22,818

inproceedings

cormac-english-etal-2022-domain

Domain-Informed Probing of wav2vec 2.0 Embeddings for Phonetic Features

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.9/

Cormac English, Patrick and Kelleher, John D. and Carson-Berndsen, Julie

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

83--91

In recent years large transformer model architectures have become available which provide a novel means of generating high-quality vector representations of speech audio. These transformers make use of an attention mechanism to generate representations enhanced with contextual and positional information from the input sequence. Previous works have explored the capabilities of these models with regard to performance in tasks such as speech recognition and speaker verification, but there has not been a significant inquiry as to the manner in which the contextual information provided by the transformer architecture impacts the representation of phonetic information within these models. In this paper, we report the results of a number of probing experiments on the representations generated by the wav2vec 2.0 model`s transformer component, with regard to the encoding of phonetic categorization information within the generated embeddings. We find that the contextual information generated by the transformer`s operation results in enhanced capture of phonetic detail by the model, and allows for distinctions to emerge in acoustic data that are otherwise difficult to separate.

null

10.18653/v1/2022.sigmorphon-1.9

null

22,819

inproceedings

habash-etal-2022-morphotactic

Morphotactic Modeling in an Open-source Multi-dialectal {A}rabic Morphological Analyzer and Generator

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.10/

Habash, Nizar and Marzouk, Reham and Khairallah, Christian and Khalifa, Salam

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

92--102

Arabic is a morphologically rich and complex language, with numerous dialectal variants. Previous efforts on Arabic morphology modeling focused on specific variants and specific domains using a range of techniques with different degrees of linguistic modeling transparency. In this paper we propose a new approach to modeling Arabic morphology with an eye towards multi-dialectness, resource openness, and easy extensibility and use. We demonstrate our approach by modeling verbs from Standard Arabic and Egyptian Arabic, within a common framework, and with high coverage.

null

10.18653/v1/2022.sigmorphon-1.10

null

22,820

inproceedings

batsuren-etal-2022-sigmorphon

The {SIGMORPHON} 2022 Shared Task on Morpheme Segmentation

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.11/

Batsuren, Khuyagbaatar and Bella, G{\'a}bor and Arora, Aryaman and Martinovic, Viktor and Gorman, Kyle and {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k and Ganbold, Amarsanaa and Dohnalov{\'a}, {\v{S}}{\'a}rka and {\v{S}}ev{\v{c}}{\'i}kov{\'a}, Magda and Pelegrinov{\'a}, Kate{\v{r}}ina and Giunchiglia, Fausto and Cotterell, Ryan and Vylomova, Ekaterina

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

103--116

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29{\%} F1 score across all languages, ranging English (93.84{\%}) to Latin (99.38{\%}). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71{\%} absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.

null

10.18653/v1/2022.sigmorphon-1.11

null

22,821

inproceedings

levine-2022-sharing

Sharing Data by Language Family: Data Augmentation for {R}omance Language Morpheme Segmentation

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.12/

Levine, Lauren

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

117--123

This paper presents a basic character level sequence-to-sequence approach to morpheme segmentation for the following Romance languages: French, Italian, and Spanish. We experiment with adding a small set of additional linguistic features, as well as with sharing training data between sister languages for morphological categories with low performance in single language base models. We find that while the additional linguistic features were generally not helpful in this instance, data augmentation between sister languages did help to raise the scores of some individual morphological categories, but did not consistently result in an overall improvement when considering the aggregate of the categories.

null

10.18653/v1/2022.sigmorphon-1.12

null

22,822

inproceedings

girrbach-2022-sigmorphon

{SIGMORPHON} 2022 Shared Task on Morpheme Segmentation Submission Description: Sequence Labelling for Word-Level Morpheme Segmentation

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.13/

Girrbach, Leander

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

124--130

We propose a sequence labelling approach to word-level morpheme segmentation. Segmentation labels are edit operations derived from a modified minimum edit distance alignment. We show that sequence labelling performs well for {\textquotedblleft}shallow segmentation{\textquotedblright} and {\textquotedblleft}canonical segmentation{\textquotedblright}, achieving 96.06 f1 score (macroaveraged over all languages in the shared task) and ranking 3rd among all participating teams. Therefore, we conclude that sequence labelling is a promising approach to morpheme segmentation.

null

10.18653/v1/2022.sigmorphon-1.13

null

22,823

inproceedings

peters-martins-2022-beyond

Beyond Characters: Subword-level Morpheme Segmentation

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.14/

Peters, Ben and Martins, Andre F. T.

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

131--138

This paper presents DeepSPIN`s submissions to the SIGMORPHON 2022 Shared Task on Morpheme Segmentation. We make three submissions, all to the word-level subtask. First, we show that entmax-based sparse sequence-tosequence models deliver large improvements over conventional softmax-based models, echoing results from other tasks. Then, we challenge the assumption that models for morphological tasks should be trained at the character level by building a transformer that generates morphemes as sequences of unigram language model-induced subwords. This subword transformer outperforms all of our character-level models and wins the word-level subtask. Although we do not submit an official submission to the sentence-level subtask, we show that this subword-based approach is highly effective there as well.

null

10.18653/v1/2022.sigmorphon-1.14

null

22,824

inproceedings

zundi-avaajargal-2022-word

Word-level Morpheme segmentation using Transformer neural network

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.15/

Zundi, Tsolmon and Avaajargal, Chinbat

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

139--143

This paper presents the submission of team NUM DI to the SIGMORPHON 2022 Task on Morpheme Segmentation Part 1, word-level morpheme segmentation. We explore the transformer neural network approach to the shared task. We develop monolingual models for world-level morpheme segmentation and focus on improving the model by using various training strategies to improve accuracy and generalization across languages.

null

10.18653/v1/2022.sigmorphon-1.15

null

22,825

inproceedings

rouhe-etal-2022-morfessor

{M}orfessor-enriched features and multilingual training for canonical morphological segmentation

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.16/

Rouhe, Aku and Gr{\"onroos, Stig-Arne and Virpioja, Sami and Creutz, Mathias and Kurimo, Mikko

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

144--151

In our submission to the SIGMORPHON 2022 Shared Task on Morpheme Segmentation, we study whether an unsupervised morphological segmentation method, Morfessor, can help in a supervised setting. Previous research has shown the effectiveness of the approach in semisupervised settings with small amounts of labeled data. The current tasks vary in data size: the amount of word-level annotated training data is much larger, but the amount of sentencelevel annotated training data remains small. Our approach is to pre-segment the input data for a neural sequence-to-sequence model with the unsupervised method. As the unsupervised method can be trained with raw text data, we use Wikipedia to increase the amount of training data. In addition, we train multilingual models for the sentence-level task. The results for the Morfessor-enriched features are mixed, showing benefit for all three sentencelevel tasks but only some of the word-level tasks. The multilingual training yields considerable improvements over the monolingual sentence-level models, but it negates the effect of the enriched features.

null

10.18653/v1/2022.sigmorphon-1.16

null

22,826

inproceedings

bodnar-2022-jb132

{JB}132 submission to the {SIGMORPHON} 2022 Shared Task 3 on Morphological Segmentation

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.17/

Bodn{\'a}r, Jan

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

152--156

This paper describes the JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morpheme Segmentation. In this paper we describe probabilistic model trained with the Expectation-Maximization algorithm, we provide the results and analyze sources of errors and general limitations of our approach. The model was implemented within our own modular probabilistic framework.

null

10.18653/v1/2022.sigmorphon-1.17

null

22,827

inproceedings

kodner-khalifa-2022-sigmorphon

{SIGMORPHON}{--}{U}ni{M}orph 2022 Shared Task 0: Modeling Inflection in Language Acquisition

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.18/

Kodner, Jordan and Khalifa, Salam

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

157--175

This year`s iteration of the SIGMORPHONUniMorph shared task on {\textquotedblleft}human-like{\textquotedblright} morphological inflection generation focuses on generalization and errors in language acquisition. Systems are trained on data sets extracted from corpora of child-directed speech in order to simulate a natural learning setting, and their predictions are evaluated against what is known about children`s developmental trajectories for three well-studied patterns: English past tense, German noun plurals, and Arabic noun plurals. Three submitted neural systems were evaluated together with two baselines. Performance was generally good, and all systems were prone to human-like over-regularization. However, all systems were also prone to non-human-like over-irregularization and nonsense productions to varying degrees. We situate this behavior in a discussion of the Past Tense Debate.

null

10.18653/v1/2022.sigmorphon-1.18

null

22,828

inproceedings

kodner-etal-2022-sigmorphon

{SIGMORPHON}{--}{U}ni{M}orph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.19/

Kodner, Jordan and Khalifa, Salam and Batsuren, Khuyagbaatar and Dolatian, Hossep and Cotterell, Ryan and Akkus, Faruk and Anastasopoulos, Antonios and Andrushko, Taras and Arora, Aryaman and Atanalov, Nona and Bella, G{\'a}bor and Budianskaya, Elena and Ghanggo Ate, Yustinus and Goldman, Omer and Guriel, David and Guriel, Simon and Guriel-Agiashvili, Silvia and Kiera{\'s}, Witold and Krizhanovsky, Andrew and Krizhanovsky, Natalia and Marchenko, Igor and Markowska, Magdalena and Mashkovtseva, Polina and Nepomniashchaya, Maria and Rodionova, Daria and Scheifer, Karina and Sorova, Alexandra and Yemelina, Anastasia and Young, Jeremiah and Vylomova, Ekaterina

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

176--203

The 2022 SIGMORPHON{--}UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the five submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world`s languages.

null

10.18653/v1/2022.sigmorphon-1.19

null

22,829

inproceedings

merzhevich-etal-2022-sigmorphon

{SIGMORPHON} 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.20/

Merzhevich, Tatiana and Gbadegoye, Nkonye and Girrbach, Leander and Li, Jingwen and Shim, Ryan Soh-Eun

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

204--211

This paper describes our participation in the 2022 SIGMORPHON-UniMorph Shared Task on Typologically Diverse and AcquisitionInspired Morphological Inflection Generation. We present two approaches: one being a modification of the neural baseline encoderdecoder model, the other being hand-coded morphological analyzers using finite-state tools (FST) and outside linguistic knowledge. While our proposed modification of the baseline encoder-decoder model underperforms the baseline for almost all languages, the FST methods outperform other systems in the respective languages by a large margin. This confirms that purely data-driven approaches have not yet reached the maturity to replace trained linguists for documentation and analysis especially considering low-resource and endangered languages.

null

10.18653/v1/2022.sigmorphon-1.20

null

22,830

inproceedings

wehrli-etal-2022-cluzh

{CLUZH} at {SIGMORPHON} 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.21/

Wehrli, Silvan and Clematide, Simon and Makarov, Peter

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

212--219

This paper describes the submissions of the team of the Department of Computational Linguistics, University of Zurich, to the SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation. Our submissions use a character-level neural transducer that operates over traditional edit actions. While this model has been found particularly wellsuited for low-resource settings, using it with large data quantities has been difficult. Existing implementations could not fully profit from GPU acceleration and did not efficiently implement mini-batch training, which could be tricky for a transition-based system. For this year`s submission, we have ported the neural transducer to PyTorch and implemented true mini-batch training. This has allowed us to successfully scale the approach to large data quantities and conduct extensive experimentation. We report competitive results for morpheme segmentation (including sharing first place in part 2 of the challenge). We also demonstrate that reducing sentence-level morpheme segmentation to a word-level problem is a simple yet effective strategy. Additionally, we report strong results in inflection generation (the overall best result for large training sets in part 1, the best results in low-resource learning trajectories in part 2). Our code is publicly available.

null

10.18653/v1/2022.sigmorphon-1.21

null

22,831

inproceedings

elsner-court-2022-osu

{OSU} at {S}ig{M}orphon 2022: Analogical Inflection With Rule Features

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.22/

Elsner, Micha and Court, Sara

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

220--225

OSU`s inflection system is a transformer whose input is augmented with an analogical exemplar showing how to inflect a different word into the target cell. In addition, alignment-based heuristic features indicate how well the exemplar is likely to match the output. OSU`s scores substantially improve over the baseline transformer for instances where an exemplar is available, though not quite matching the challenge winner. In Part 2, the system shows a tendency to over-apply the majority pattern in English, but not Arabic.

null

10.18653/v1/2022.sigmorphon-1.22

null

22,832

inproceedings

yang-etal-2022-generalizing

Generalizing Morphological Inflection Systems to Unseen Lemmas

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.23/

Yang, Changbing and Yang, Ruixin (Ray) and Nicolai, Garrett and Silfverberg, Miikka

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

226--235

This paper presents experiments on morphological inflection using data from the SIGMORPHON-UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection. We present a transformer inflection system, which enriches the standard transformer architecture with reverse positional encoding and type embeddings. We further apply data hallucination and lemma copying to augment training data. We train models using a two-stage procedure: (1) We first train on the augmented training data using standard backpropagation and teacher forcing. (2) We then continue training with a variant of the scheduled sampling algorithm dubbed student forcing. Our system delivers competitive performance under the small and large data conditions on the shared task datasets.

null

10.18653/v1/2022.sigmorphon-1.23

null

22,833

inproceedings

kakolu-ramarao-etal-2022-heimorph

{H}ei{M}orph at {SIGMORPHON} 2022 Shared Task on Morphological Acquisition Trajectories

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.24/

Kakolu Ramarao, Akhilesh and Zinova, Yulia and Tang, Kevin and van de Vijver, Ruben

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

236--239

This paper presents the submission by the HeiMorph team to the SIGMORPHON 2022 task 2 of Morphological Acquisition Trajectories. Across all experimental conditions, we have found no evidence for the so-called Ushaped development trajectory. Our submitted systems achieve an average test accuracies of 55.5{\%} on Arabic, 67{\%} on German and 73.38{\%} on English. We found that, bigram hallucination provides better inferences only for English and Arabic and only when the number of hallucinations remains low.

null

10.18653/v1/2022.sigmorphon-1.24

null

22,834

inproceedings

sherbakov-vylomova-2022-morphology

Morphology is not just a naive {B}ayes {--} {U}ni{M}elb Submission to {SIGMORPHON} 2022 {ST} on Morphological Inflection

Nicolai, Garrett and Chodroff, Eleanor

jul

2022

Seattle, Washington

Association for Computational Linguistics

https://aclanthology.org/2022.sigmorphon-1.25/

Sherbakov, Andreas and Vylomova, Ekaterina

Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

240--246

The paper describes the Flexica team`s submission to the SIGMORPHON 2022 Shared Task 1 Part 1: Typologically Diverse Morphological Inflection. Our team submitted a nonneural system that extracted transformation patterns from alignments between a lemma and inflected forms. For each inflection category, we chose a pattern based on its abstractness score. The system outperformed the non-neural baseline, the extracted patterns covered a substantial part of possible inflections. However, we discovered that such score that does not account for all possible combinations of string segments as well as morphosyntactic features is not sufficient for a certain proportion of inflection cases.

null

10.18653/v1/2022.sigmorphon-1.25

null

22,835

inproceedings

ohashi-higashinaka-2022-post

Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.1/

Ohashi, Atsumoto and Higashinaka, Ryuichiro

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

1--13

Many studies have proposed methods for optimizing the dialogue performance of an entire pipeline task-oriented dialogue system by jointly training modules in the system using reinforcement learning. However, these methods are limited in that they can only be applied to modules implemented using trainable neural-based methods. To solve this problem, we propose a method for optimizing a pipeline system composed of modules implemented with arbitrary methods for dialogue performance. With our method, neural-based components called post-processing networks (PPNs) are installed inside such a system to post-process the output of each module. All PPNs are updated to improve the overall dialogue performance of the system by using reinforcement learning, not necessitating each module to be differentiable. Through dialogue simulation and human evaluation on the MultiWOZ dataset, we show that our method can improve the dialogue performance of pipeline systems consisting of various modules.

null

10.18653/v1/2022.sigdial-1.1

null

22,837

inproceedings

hidey-etal-2022-reducing

Reducing Model Churn: Stable Re-training of Conversational Agents

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.2/

Hidey, Christopher and Liu, Fei and Goel, Rahul

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

14--25

Retraining modern deep learning systems can lead to variations in model performance even when trained using the same data and hyper-parameters by simply using different random seeds. This phenomenon is known as model churn or model jitter. This issue is often exacerbated in real world settings, where noise may be introduced in the data collection process. In this work we tackle the problem of stable retraining with a novel focus on structured prediction for conversational semantic parsing. We first quantify the model churn by introducing metrics for agreement between predictions across multiple retrainings. Next, we devise realistic scenarios for noise injection and demonstrate the effectiveness of various churn reduction techniques such as ensembling and distillation. Lastly, we discuss practical trade-offs between such techniques and show that co-distillation provides a sweet spot in terms of churn reduction with only a modest increase in resource usage.

null

10.18653/v1/2022.sigdial-1.2

null

22,838

inproceedings

lin-etal-2022-knowledge

Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.3/

Lin, Yen Ting and Papangelis, Alexandros and Kim, Seokhwan and Hakkani-Tur, Dilek

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

26--38

While rich, open-domain textual data are generally available and may include interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for language processing tasks, and are usually in a non-conversational format. In this work, we take a step towards automatically generating conversational data using Generative Conversational Networks, aiming to benefit from the breadth of available language and knowledge data, and train open domain social conversational agents. We evaluate our approach on conversations with and without knowledge on the Topical Chat dataset using automatic metrics and human evaluators. Our results show that for conversations without knowledge grounding, GCN can generalize from the seed data, producing novel conversations that are less relevant but more engaging and for knowledge-grounded conversations, it can produce more knowledge-focused, fluent, and engaging conversations. Specifically, we show that for open-domain conversations with 10{\%} of seed data, our approach performs close to the baseline that uses 100{\%} of the data, while for knowledge-grounded conversations, it achieves the same using only 1{\%} of the data, on human ratings of engagingness, fluency, and relevance.

null

10.18653/v1/2022.sigdial-1.3

null

22,839

inproceedings

bergman-etal-2022-guiding

Guiding the Release of Safer {E}2{E} Conversational {AI} through Value Sensitive Design

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.4/

Bergman, A. Stevie and Abercrombie, Gavin and Spruit, Shannon and Hovy, Dirk and Dinan, Emily and Boureau, Y-Lan and Rieser, Verena

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

39--52

Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, open-domain conversations with humans. However, these models are often trained on large datasets from the Internet and, as a result, may learn undesirable behaviours from this data, such as toxic or otherwise harmful language. Thus, researchers must wrestle with how and when to release these models. In this paper, we survey recent and related work to highlight tensions between values, potential positive impact, and potential harms. We also provide a framework to support practitioners in deciding whether and how to release these models, following the tenets of value-sensitive design.

null

10.18653/v1/2022.sigdial-1.4

null

22,840

inproceedings

lai-etal-2022-controllable

Controllable User Dialogue Act Augmentation for Dialogue State Tracking

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.5/

Lai, Chun-Mao and Hsu, Ming-Hao and Huang, Chao-Wei and Chen, Yun-Nung

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

53--61

Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1.

null

10.18653/v1/2022.sigdial-1.5

null

22,841

inproceedings

kawaletz-etal-2022-developing

Developing an argument annotation scheme based on a semantic classification of arguments

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.6/

Kawaletz, Lea and Dorgeloh, Heidrun and Conrad, Stefan and Bekcic, Zeljko

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

62--67

Corpora of argumentative discourse are commonly analyzed in terms of argumentative units, consisting of claims and premises. Both argument detection and classification are complex discourse processing tasks. Our paper introduces a semantic classification of arguments that can help to facilitate argument detection. We report on our experiences with corpus annotations using a function-based classification of arguments and a procedure for operationalizing the scheme by using semantic templates.

null

10.18653/v1/2022.sigdial-1.6

null

22,842

inproceedings

li-etal-2022-multi

Multi-Task Learning for Depression Detection in Dialogs

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.7/

Li, Chuyuan and Braud, Chlo{\'e} and Amblard, Maxime

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

68--75

Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others. This work examines depression signals in dialogs, a less studied setting that suffers from data sparsity. We hypothesize that depression and emotion can inform each other, and we propose to explore the influence of dialog structure through topic and dialog act prediction. We investigate a Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned jointly with dialog-tailored hierarchical modeling. We experiment on the DAIC and DailyDialog corpora {--} both contain dialogs in English {--} and show important improvements over state-of-the-art on depression detection (at best 70.6{\%} F1), which demonstrates the correlation of depression with emotion and dialog organization and the power of MTL to leverage information from different sources.

null

10.18653/v1/2022.sigdial-1.7

null

22,843

inproceedings

ludusan-schuppler-2022-laugh

To laugh or not to laugh? The use of laughter to mark discourse structure

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.8/

Ludusan, Bogdan and Schuppler, Barbara

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

76--82

A number of cues, both linguistic and non-linguistic, have been found to mark discourse structure in conversation. This paper investigates the role of laughter, one of the most encountered non-verbal vocalizations in human communication, in the signalling of turn boundaries. We employ a corpus of informal dyadic conversations to determine the likelihood of laughter at the end of speaker turns and to establish the potential role of laughter in discourse organization. Our results show that, on average, about 10{\%} of the turns are marked by laughter, but also that the marking is subject to individual variation, as well as effects of other factors, such as the type of relationship between speakers. More importantly, we find that turn ends are twice more likely than transition relevance places to be marked by laughter, suggesting that, indeed, laughter plays a role in marking discourse structure.

null

10.18653/v1/2022.sigdial-1.8

null

22,844

inproceedings

mendonca-etal-2022-qualityadapt

{Q}uality{A}dapt: an Automatic Dialogue Quality Estimation Framework

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.9/

Mendonca, John and Lavie, Alon and Trancoso, Isabel

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

83--90

Despite considerable advances in open-domain neural dialogue systems, their evaluation remains a bottleneck. Several automated metrics have been proposed to evaluate these systems, however, they mostly focus on a single notion of quality, or, when they do combine several sub-metrics, they are computationally expensive. This paper attempts to solve the latter: QualityAdapt leverages the Adapter framework for the task of Dialogue Quality Estimation. Using well defined semi-supervised tasks, we train adapters for different subqualities and score generated responses with AdapterFusion. This compositionality provides an easy to adapt metric to the task at hand that incorporates multiple subqualities. It also reduces computational costs as individual predictions of all subqualities are obtained in a single forward pass. This approach achieves comparable results to state-of-the-art metrics on several datasets, whilst keeping the previously mentioned advantages.

null

10.18653/v1/2022.sigdial-1.9

null

22,845

inproceedings

cordier-etal-2022-graph

Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.10/

Cordier, Thibault and Urvoy, Tanguy and Lef{\`e}vre, Fabrice and Rojas Barahona, Lina M.

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

91--100

Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans. In practice, they may have to handle simultaneously several domains and tasks. The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with multi-domain dialogues. However, learning with reinforcement in such context becomes difficult because the state-action dimension is larger while the reward signal remains scarce. Our experimental results suggest that structured policies based on graph neural networks combined with different degrees of imitation learning can effectively handle multi-domain dialogues. The reported experiments underline the benefit of structured policies over standard policies.

null

10.18653/v1/2022.sigdial-1.10

null

22,846

inproceedings

huynh-etal-2022-dialport

The {D}ial{P}ort tools

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.11/

Huynh, Jessica and Mehri, Shikib and Jiao, Cathy and Eskenazi, Maxine

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

101--106

The DialPort project (\url{http://dialport.org/}), funded by the National Science Foundation (NSF), covers a group of tools and services that aim at fulfilling the needs of the dialog research community. Over the course of six years, several offerings have been created, including the DialPort Portal and DialCrowd. This paper describes these contributions, which will be demoed at SIGDIAL, including implementation, prior studies, corresponding discoveries, and the locations at which the tools will remain freely available to the community going forward.

null

10.18653/v1/2022.sigdial-1.11

null

22,847

inproceedings

kawai-etal-2022-simultaneous

Simultaneous Job Interview System Using Multiple Semi-autonomous Agents

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.12/

Kawai, Haruki and Muraki, Yusuke and Yamamoto, Kenta and Lala, Divesh and Inoue, Koji and Kawahara, Tatsuya

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

107--110

In recent years, spoken dialogue systems have been applied to job interviews where an applicant talks to a system that asks pre-defined questions, called on-demand and self-paced job interviews. We propose a simultaneous job interview system, where one interviewer can conduct one-on-one interviews with multiple applicants simultaneously by cooperating with the multiple autonomous job interview dialogue systems. However, it is challenging for interviewers to monitor and understand all the parallel interviews done by the autonomous system at the same time. As a solution to this issue, we implemented two automatic dialogue understanding functions: (1) response evaluation of each applicant`s responses and (2) keyword extraction as a summary of the responses. It is expected that interviewers, as needed, can intervene in one dialogue and smoothly ask a proper question that elaborates the interview. We report a pilot experiment where an interviewer conducted simultaneous job interviews with three candidates.

null

10.18653/v1/2022.sigdial-1.12

null

22,848

inproceedings

gella-etal-2022-dialog

Dialog Acts for Task Driven Embodied Agents

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.13/

Gella, Spandana and Padmakumar, Aishwarya and Lange, Patrick and Hakkani-Tur, Dilek

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

111--123

Embodied agents need to be able to interact in natural language {--} understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users. In this work, we propose a set of dialog acts for modelling such dialogs and annotate the TEACh dataset that includes over 3,000 situated, task oriented conversations (consisting of 39.5k utterances in total) with dialog acts. To our knowledge,TEACh-DA is the first large scale dataset of dialog act annotations for embodied task completion. Furthermore, we demonstrate the use of this annotated dataset in training models for tagging the dialog acts of a given utterance, predicting the dialog act of the next response given a dialog history, and use the dialog acts to guide agent`s non-dialog behaviour. In particular, our experiments on the TEACh Execution from Dialog History task where the model predicts the sequence of low level actions to be executed in the environment for embodied task completion, demonstrate that dialog acts can improve end performance by up to 2 points compared to the system without dialog acts.

null

10.18653/v1/2022.sigdial-1.13

null

22,849

inproceedings

torres-foncesca-etal-2022-symbol

Symbol and Communicative Grounding through Object Permanence with a Mobile Robot

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.14/

Torres-Fonseca, Josue and Henry, Catherine and Kennington, Casey

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

124--134

Object permanence is the ability to form and recall mental representations of objects even when they are not in view. Despite being a crucial developmental step for children, object permanence has had only some exploration as it relates to symbol and communicative grounding in spoken dialogue systems. In this paper, we leverage SLAM as a module for tracking object permanence and use a robot platform to move around a scene where it discovers objects and learns how they are denoted. We evaluated by comparing our system`s effectiveness at learning words from human dialogue partners both with and without object permanence. We found that with object permanence, human dialogue partners spoke with the robot and the robot correctly identified objects it had learned about significantly more than without object permanence, which suggests that object permanence helped facilitate communicative and symbol grounding.

null

10.18653/v1/2022.sigdial-1.14

null

22,850

inproceedings

fernau-etal-2022-towards

Towards Personality-Aware Chatbots

Lemon, Oliver and Hakkani-Tur, Dilek and Li, Junyi Jessy and Ashrafzadeh, Arash and Garcia, Daniel Hern{\'a}ndez and Alikhani, Malihe and Vandyke, David and Du{\v{s}}ek, Ond{\v{r}}ej

sep

2022

Edinburgh, UK

Association for Computational Linguistics

https://aclanthology.org/2022.sigdial-1.15/

Fernau, Daniel and Hillmann, Stefan and Feldhus, Nils and Polzehl, Tim and M{\"oller, Sebastian

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

135--145

Chatbots are increasingly used to automate operational processes in customer service. However, most chatbots lack adaptation towards their users which may results in an unsatisfactory experience. Since knowing and meeting personal preferences is a key factor for enhancing usability in conversational agents, in this study we analyze an adaptive conversational agent that can automatically adjust according to a user`s personality type carefully excerpted from the Myers-Briggs type indicators. An experiment including 300 crowd workers examined how typifications like extroversion/introversion and thinking/feeling can be assessed and designed for a conversational agent in a job recommender domain. Our results validate the proposed design choices, and experiments on a user-matched personality typification, following the so-called law of attraction rule, show a significant positive influence on a range of selected usability criteria such as overall satisfaction, naturalness, promoter score, trust and appropriateness of the conversation.

null

10.18653/v1/2022.sigdial-1.15

null

22,851