Challenge: Using a CC BY-NC-SA 4.0 license, this corpus contains parallel sign language videos and spoken language subtitles.
Approach: They introduce SwissSLi, the first sign language corpus that contains parallel data of all three Swiss sign languages.
Outcome: The proposed corpus contains parallel sign language videos and spoken language subtitles.

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How to Align Multiple Signed Language Corpora for Better Sign-to-Sign Translations? (2025.naacl-long)

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Challenge: despite the growing need for advanced signing technologies, signed language resources remain scarce.
Approach: They propose a linguistically informed alignment algorithm that matches instances between signed languages . they compare similarities and differences across three signed languages to develop a model .
Outcome: The proposed algorithm performs well on automatic metrics for sign-to-sign translation and generation.
Alignment Data base for a Sign Language Concordancer (2020.lrec-1)

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Challenge: a new study examines the need for sign language translators to have tools similar to text-to-text translation.
Approach: They propose to use a concordancer to search for parallel Franch-LSF segments . they use dozens of short news clips and 120 SL videos to align them manually .
Outcome: The proposed data base will be searched using a concordancer and expand in the future.
Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards (2026.acl-long)

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Challenge: Existing benchmarks fail to reflect real-world communication needs and are limited in their coverage.
Approach: They present a comprehensive index of sign-language datasets, covering 120 resources across 35 sign languages.
Outcome: The proposed index covers 120 resources across 35 sign languages.
J-Shuwa: A Large-Scale Web-Collected Japanese Sign Language-Japanese Parallel Corpus (2026.findings-acl)

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Challenge: Japanese Sign Language (JSL) is a low-resource sign language that has received limited attention in the AI community due to the lack of large-scale, publicly available parallel corpora.
Approach: They propose a large-scale JSL-Japanese parallel corpus constructed from YouTube videos with hard-coded subtitles and closed captions.
Outcome: The proposed model is effective for training models and can be used for future research across a wide range of tasks.
JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language Processing (2023.findings-emnlp)

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Challenge: Existing sign language datasets are limited and skewed towards high-income sign languages, mainly those from high-risk countries.
Approach: They propose a large and highly multilingual dataset for sign language translation: JWSign.
Outcome: The proposed dataset consists of 2,530 hours of Bible translations in 98 sign languages, featuring more than 1,500 individual signers.
SMILE Swiss German Sign Language Dataset (L18-1)

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Challenge: The goal of an ongoing three-year project in Switzerland is to pioneer an assessment system for lexical signs of Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS) that relies on sign language recognition.
Approach: The goal of the project is to pioneer an assessment system for lexical signs of Swiss German Sign Language that relies on sign language recognition.
Outcome: The system will give adult L2 learners of DSGS feedback on the correctness of the manual parameters (handshape, hand position, location, and movement) of isolated signs they produce.
Deep JSLC: A Multimodal Corpus Collection for Data-driven Generation of Japanese Sign Language Expressions (L18-1)

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Challenge: Existing technologies for CG-supported data display are not able to depict all relevant features of a natural signing sequence such as facial expression, spatial references or inter-sign movement.
Approach: They collected a corpus of Japanese Sign Language sentences for deep neural network learning.
Outcome: The proposed model could be used to train language features in Japanese Sign Language (JSL)
Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville) (L18-1)

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Challenge: BULB project aims to provide tools to language documentation and description for unwritten languages . language-based technologies are needed to support the collection of data and to provide linguistic documentation for the languages.
Approach: This paper presents multimodal and parallel data collections in Mboshi, as part of the French-German BULB project.
Outcome: The proposed data collection includes pictures and videos documenting social practices, agriculture, wildlife and plants.
Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation (2022.lrec-1)

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Challenge: a new corpus of french Sign Language (LSF) data is created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer.
Approach: They propose to use a French Sign Language corpus called "Rosetta-LSF" it is intended to support studies on automatic translation of written French into LSF .
Outcome: The proposed corpus supports future studies on automatic translation of written French into LSF, rendered through animation of a virtual signer.
LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition (2020.lrec-1)

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Challenge: a corpus of sentence-aligned triples of German audio, German text, and English translation is available for speech recognition . a large corpus is available to date for end-to-end speech translation based on parallel data .
Approach: They present a corpus of sentence-aligned triples of German audio, German text, and English translation based on German audio books.
Outcome: The proposed corpus is the largest resource for German speech recognition and for end-to-end German-to English speech translation.

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