Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL)

Dimitar Shterionov (Editor)

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Association for Machine Translation in the Americas
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Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL)
Dimitar Shterionov

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Data Augmentation for Sign Language Gloss Translation
Amit Moryossef | Kayo Yin | Graham Neubig | Yoav Goldberg

Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss to-text translation, where a gloss is a sequence of transcribed spoken-language words in the order in which they are signed. We focus here on gloss-to-text translation, which we treat as a low-resource neural machine translation (NMT) problem. However, unlike traditional low resource NMT, gloss-to-text translation differs because gloss-text pairs often have a higher lexical overlap and lower syntactic overlap than pairs of spoken languages. We exploit this lexical overlap and handle syntactic divergence by proposing two rule-based heuristics that generate pseudo-parallel gloss-text pairs from monolingual spoken language text. By pre-training on this synthetic data, we improve translation from American Sign Language (ASL) to English and German Sign Language (DGS) to German by up to 3.14 and 2.20 BLEU, respectively.

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Is “good enough” good enough? Ethical and responsible development of sign language technologies
Maartje De Meulder

This paper identifies some common and specific pitfalls in the development of sign language technologies targeted at deaf communities, with a specific focus on signing avatars. It makes the call to urgently interrogate some of the ideologies behind those technologies, including issues of ethical and responsible development. The paper addresses four separate and interlinked issues: ideologies about deaf people and mediated communication, bias in data sets and learning, user feedback, and applications of the technologies. The paper ends with several take away points for both technology developers and deaf NGOs. Technology developers should give more consideration to diversifying their team and working interdisciplinary, and be mindful of the biases that inevitably creep into data sets. There should also be a consideration of the technologies’ end users. Sign language interpreters are not the end users nor should they be seen as the benchmark for language use. Technology developers and deaf NGOs can engage in a dialogue about how to prioritize application domains and prioritize within application domains. Finally, deaf NGOs policy statements will need to take a longer view, and use avatars to think of a significantly better system compared to what sign language interpreting services can provide.

Sign and Search: Sign Search Functionality for Sign Language Lexica
Manolis Fragkiadakis | Peter van der Putten

Sign language lexica are a useful resource for researchers and people learning sign languages. Current implementations allow a user to search a sign either by its gloss or by selecting its primary features such as handshape and location. This study focuses on exploring a reverse search functionality where a user can sign a query sign in front of a webcam and retrieve a set of matching signs. By extracting different body joints combinations (upper body, dominant hand’s arm and wrist) using the pose estimation framework OpenPose, we compare four techniques (PCA, UMAP, DTW and Euclidean distance) as distance metrics between 20 query signs, each performed by eight participants on a 1200 sign lexicon. The results show that UMAP and DTW can predict a matching sign with an 80% and 71% accuracy respectively at the top-20 retrieved signs using the movement of the dominant hand arm. Using DTW and adding more sign instances from other participants in the lexicon, the accuracy can be raised to 90% at the top-10 ranking. Our results suggest that our methodology can be used with no training in any sign language lexicon regardless of its size.

The Myth of Signing Avatars
John C. McDonald | Rosalee Wolfe | Eleni Efthimiou | Evita Fontinea | Frankie Picron | Davy Van Landuyt | Tina Sioen | Annelies Braffort | Michael Filhol | Sarah Ebling | Thomas Hanke | Verena Krausneker

Development of automatic translation between signed and spoken languages has lagged behind the development of automatic translation between spoken languages, but it is a common misperception that extending machine translation techniques to include signed languages should be a straightforward process. A contributing factor is the lack of an acceptable method for displaying sign language apart from interpreters on video. This position paper examines the challenges of displaying a signed language as a target in automatic translation, analyses the underlying causes and suggests strategies to develop display technologies that are acceptable to sign language communities.

AVASAG: A German Sign Language Translation System for Public Services (short paper)
Fabrizio Nunnari | Judith Bauerdiek | Lucas Bernhard | Cristina España-Bonet | Corinna Jäger | Amelie Unger | Kristoffer Waldow | Sonja Wecker | Elisabeth André | Stephan Busemann | Christian Dold | Arnulph Fuhrmann | Patrick Gebhard | Yasser Hamidullah | Marcel Hauck | Yvonne Kossel | Martin Misiak | Dieter Wallach | Alexander Stricker

This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services. We describe the scientific innovation points (geometry-based SL-description, 3D animation and video corpus, simplified annotation scheme, motion capture strategy) and the overall translation pipeline.

Using Computer Vision to Analyze Non-manual Marking of Questions in KRSL
Anna Kuznetsova | Alfarabi Imashev | Medet Mukushev | Anara Sandygulova | Vadim Kimmelman

This paper presents a study that compares non-manual markers of polar and wh-questions to statements in Kazakh-Russian Sign Language (KRSL) in a dataset collected for NLP tasks. The primary focus of the study is to demonstrate the utility of computer vision solutions for the linguistic analysis of non-manuals in sign languages, although additional corrections are required to account for biases in the output. To this end, we analyzed recordings of 10 triplets of sentences produced by 9 native signers using both manual annotation and computer vision solutions (such as OpenFace). We utilize and improve the computer vision solution, and briefly describe the results of the linguistic analysis.

Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task
Xuan Zhang | Kevin Duh

A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.

Automatic generation of a 3D sign language avatar on AR glasses given 2D videos of human signers
Lan Thao Nguyen | Florian Schicktanz | Aeneas Stankowski | Eleftherios Avramidis

In this paper we present a prototypical implementation of a pipeline that allows the automatic generation of a German Sign Language avatar from 2D video material. The presentation is accompanied by the source code. We record human pose movements during signing with computer vision models. The joint coordinates of hands and arms are imported as landmarks to control the skeleton of our avatar. From the anatomically independent landmarks, we create another skeleton based on the avatar’s skeletal bone architecture to calculate the bone rotation data. This data is then used to control our human 3D avatar. The avatar is displayed on AR glasses and can be placed virtually in the room, in a way that it can be perceived simultaneously to the verbal speaker. In further work it is aimed to be enhanced with speech recognition and machine translation methods for serving as a sign language interpreter. The prototype has been shown to people of the deaf and hard-of-hearing community for assessing its comprehensibility. Problems emerged with the transferred hand rotations, hand gestures were hard to recognize on the avatar due to deformations like twisted finger meshes.

Online Evaluation of Text-to-sign Translation by Deaf End Users: Some Methodological Recommendations (short paper)
Floris Roelofsen | Lyke Esselink | Shani Mende-Gillings | Maartje de Meulder | Nienke Sijm | Anika Smeijers

We present a number of methodological recommendations concerning the online evaluation of avatars for text-to-sign translation, focusing on the structure, format and length of the questionnaire, as well as methods for eliciting and faithfully transcribing responses

Frozen Pretrained Transformers for Neural Sign Language Translation
Mathieu De Coster | Karel D’Oosterlinck | Marija Pizurica | Paloma Rabaey | Severine Verlinden | Mieke Van Herreweghe | Joni Dambre

One of the major challenges in sign language translation from a sign language to a spoken language is the lack of parallel corpora. Recent works have achieved promising results on the RWTH-PHOENIX-Weather 2014T dataset, which consists of over eight thousand parallel sentences between German sign language and German. However, from the perspective of neural machine translation, this is still a tiny dataset. To improve the performance of models trained on small datasets, transfer learning can be used. While this has been previously applied in sign language translation for feature extraction, to the best of our knowledge, pretrained language models have not yet been investigated. We use pretrained BERT-base and mBART-50 models to initialize our sign language video to spoken language text translation model. To mitigate overfitting, we apply the frozen pretrained transformer technique: we freeze the majority of parameters during training. Using a pretrained BERT model, we outperform a baseline trained from scratch by 1 to 2 BLEU-4. Our results show that pretrained language models can be used to improve sign language translation performance and that the self-attention patterns in BERT transfer in zero-shot to the encoder and decoder of sign language translation models.

Defining meaningful units. Challenges in sign segmentation and segment-meaning mapping (short paper)
Mirella De Sisto | Dimitar Shterionov | Irene Murtagh | Myriam Vermeerbergen | Lorraine Leeson

This paper addresses the tasks of sign segmentation and segment-meaning mapping in the context of sign language (SL) recognition. It aims to give an overview of the linguistic properties of SL, such as coarticulation and simultaneity, which make these tasks complex. A better understanding of SL structure is the necessary ground for the design and development of SL recognition and segmentation methodologies, which are fundamental for machine translation of these languages. Based on this preliminary exploration, a proposal for mapping segments to meaning in the form of an agglomerate of lexical and non-lexical information is introduced.