pwc-archive/papers-with-abstracts huggingface dataset:
Applying the Transcription System Typannot to Mouth Gestures
Authors: Claire Danet, Chloé Thomas, Adrien Contesse, Morgane Rébulard, Claudia S. Bianchini, Léa Chevrefils, Patrick Doan
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

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.
Libras Portal: A Way of Documentation, a Way of Sharing
Authors: Ronice de Quadros, Renata Krusser, Daniela Saito
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

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.
Representation and Synthesis of Geometric Relocations
Authors: Michael Filhol, John McDonald
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: gaze redirection

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.
Sign Language Phonetic Annotator-Analyzer: Open-Source Software for Form-Based Analysis of Sign Languages
Authors: Kathleen Currie Hall, Yurika Aonuki, Kaili Vesik, April Poy, Nico Tolmie
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Form

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 ‘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.
MY DGS – ANNIS: ANNIS and the Public DGS Corpus
Authors: Amy Isard, Reiner Konrad
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

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.
Outreach and Science Communication in the DGS-Korpus Project: Accessibility of Data and the Benefit of Interactive Exchange between Communities
Authors: Elena Jahn, Calvin Khan, Annika Herrmann
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Public Relations

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
MC-TRISLAN: A Large 3D Motion Capture Sign Language Data-set
Authors: Pavel Jedlička, Zdeněk Krňoul, Milos Zelezny, Ludek Muller
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Machine Translation, Translation

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.
A Machine Learning-based Segmentation Approach for Measuring Similarity between Sign Languages
Authors: Tonni Das Jui, Gissella Bejarano, Pablo Rivas
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Machine Translation, Video Segmentation, Video Semantic Segmentation

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.
The Sign Language Dataset Compendium: Creating an Overview of Digital Linguistic Resources
Authors: Maria Kopf, Marc Schulder, Thomas Hanke
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

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.
Making Sign Language Corpora Comparable: A Study of Palm-Up and Throw-Away in Polish Sign Language, German Sign Language, and Russian Sign Language
Authors: Anna Kuder
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: All

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.
Open Repository of the Polish Sign Language Corpus: Publication Project of the Polish Sign Language Corpus
Authors: Anna Kuder, Joanna Wójcicka, Piotr Mostowski, Paweł Rutkowski
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

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 język 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.
Functional Data Analysis of Non-manual Marking of Questions in Kazakh-Russian Sign Language
Authors: Anna Kuznetsova, Alfarabi Imashev, Medet Mukushev, Anara Sandygulova, Vadim Kimmelman
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

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.
Two New AZee Production Rules Refining Multiplicity in French Sign Language
Authors: Emmanuella Martinod, Claire Danet, Michael Filhol
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Vocal Bursts Valence Prediction

This paper is a contribution to sign language (SL) modeling. We focus on the hitherto imprecise notion of “Multiplicity”, 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.
Language Planning in Action: Depiction as a Driver of New Terminology in Irish Sign Language
Authors: Rachel Moiselle, Lorraine Leeson
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Machine Translation

In this paper, we examine the linguistic phenomenon known as ‘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 ‘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.
Facilitating the Spread of New Sign Language Technologies across Europe
Authors: Hope Morgan, Onno Crasborn, Maria Kopf, Marc Schulder, Thomas Hanke
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Translation

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.
ISL-LEX v.1: An Online Lexical Resource of Israeli Sign Language
Authors: Hope Morgan, Wendy Sandler, Rose Stamp, Rama Novogrodsky
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

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.
Towards Large Vocabulary Kazakh-Russian Sign Language Dataset: KRSL-OnlineSchool
Authors: Medet Mukushev, Aigerim Kydyrbekova, Vadim Kimmelman, Anara Sandygulova
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6
Tasks: Sign Language Translation, Speech-to-Text

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 https://krslproject.github.io/online-school/
Towards Semi-automatic Sign Language Annotation Tool: SLAN-tool
Authors: Medet Mukushev, Arman Sabyrov, Madina Sultanova, Vadim Kimmelman, Anara Sandygulova
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

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 https://slan-tool.com.
Resources for Computer-Based Sign Recognition from Video, and the Criticality of Consistency of Gloss Labeling across Multiple Large ASL Video Corpora
Authors: Carol Neidle, Augustine Opoku, Carey Ballard, Konstantinos M. Dafnis, Evgenia Chroni, Dimitri Metaxas
Date: 2022-06-01 00:00:00
Proceeding: SignLang (LREC) 2022 6

The WLASL purports to be “the largest video dataset for Word-Level American Sign Language (ASL) recognition.” 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.