Challenge: Sign language is an effective non-verbal communication mode for the hearingimpaired people.
Approach: They propose a three-form scheme to represent dynamic CSL gestures using a word-based dataset.
Outcome: The proposed framework integrates the local sequential sensor data derived from the wearable-based CSL gestures with the global, fine-grained skeleton representations captured from video-based gestures simultaneously.

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WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language (2022.acl-short)

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Challenge: Signed Language Processing (SLP) is a major form of NLP, but has been overlooked by the NLP community.
Approach: They leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties.
Outcome: The proposed model outperforms existing approaches on signs unobserved during training.
Handshape-Aware Sign Language Recognition: Extended Datasets and Exploration of Handshape-Inclusive Methods (2023.findings-emnlp)

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Challenge: Existing work on sign language recognition encodes videos without acknowledging phonological attributes of signs.
Approach: They propose a single-encoder network and a dual-encoding network for handshape-inclusive sign language recognition.
Outcome: The proposed methods outperform baseline methods in the PHOENIX14T-HS dataset . the proposed methods consistently outperformed baseline methods .
A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News (2024.lrec-main)

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Challenge: a new dataset is being developed to enrich resources for sign language research . the dataset is 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words.
Approach: They introduce a new Hong Kong sign language dataset called TVB-HKSL-News . the dataset is collected from a TV news program and contains sign videos . they aim to support research in sign language recognition and translation .
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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.
CS2W: A Chinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types (2023.emnlp-main)

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Challenge: Existing datasets focus on a single type of spoken style, such as disfluencies.
Approach: They propose a Chinese Spoken-to-Written style conversion dataset with 7,237 spoken sentences extracted from transcribed conversational texts.
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Multilingual Gloss-free Sign Language Translation: Towards Building a Sign Language Foundation Model (2025.acl-short)

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Challenge: Existing studies focus on translating a single SL into a spoken language (one-to-one SLT) however, multilingual SLT remains unexplored due to language conflicts and alignment difficulties across SLs and spoken languages.
Approach: They propose a multilingual gloss-free model that can be used to translate a single SL into a spoken language and generate a token-level SL identification and spoken text.
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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.
Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition (L18-1)

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Challenge: a new general framework for sign recognition from monocular video is presented . the framework exploits state-of-the-art learning methods while incorporating features based on what we know about the linguistic composition of lexical signs.
Approach: They propose a general framework for sign recognition from monocular video . they exploit state-of-the-art learning methods while incorporating features from linguistic information .
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Open-Domain Sign Language Translation Learned from Online Video (2022.emnlp-main)

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Challenge: Existing work on sign language translation has focused mainly on data collected in controlled environments or domains, which limits its applicability to real-world settings.
Approach: They propose to use sign search as a pretext task and fusion of mouthing and handshape features to improve sign language translation in real-world settings.
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Automatic Gloss-level Data Augmentation for Sign Language Translation (2022.lrec-1)

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Challenge: Existing methods for enhancing sign language text data are insufficient . fewer studies have been performed on text data augmentation compared to video data .
Approach: They propose three methods to augment sign language text data using Korean sign language gloss dictionary.
Outcome: The proposed method improves translation performance by 0.204 and 0.170 compared to the original data.

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