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Including Signed Languages in Natural Language Processing

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arxiv 2105.05222 v2 pith:WG5E6UTX submitted 2021-05-11 cs.CL cs.AIcs.LG

Kayo Yin , Amit Moryossef , Julie Hochgesang , Yoav Goldberg , Malihe Alikhani This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords signedlanguageslanguagelinguisticnaturalprocessingresearchmodeling
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Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in the direction of research.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sign Language Question Answering: A New Task, Benchmark, and Baseline for Sign Language Understanding

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Sign Language QA benchmarks are introduced from PHOENIX14T and CSL-Daily via template-generated questions, and a question-conditioned baseline outperforms video-language and cascaded baselines.

  2. Perspectives on Capturing Emotional Expressiveness in Sign Language

    cs.HC 2025-05 conditional novelty 5.0 of 10

    Interviews with eight signers show that emotional meaning in sign language comes from manual and non-manual cues built into the grammar, so translation tools should model faces, bodies, and signing dynamics, not just signs.

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