WW-CSL: A New Dataset for Word-Based Wearable Chinese Sign Language Detection (2024.lrec-main)
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| 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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| Challenge: | Signed Language Processing (SLP) is a major form of NLP, but has been overlooked by the NLP community. |
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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. |
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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. |
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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. |
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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. |
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| Challenge: | Existing benchmarks fail to reflect real-world communication needs and are limited in their coverage. |
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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. |
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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. |
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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 . |
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