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Open-Domain Sign Language Translation Learned from Online Video

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arxiv 2205.12870 v2 pith:EZXNMFJV submitted 2022-05-25 cs.CV cs.CL

Bowen Shi , Diane Brentari , Greg Shakhnarovich , Karen Livescu This is my paper · ORCID
classification cs.CVcs.CL
keywords languagesigntranslationdataopenaslavailablecollecteddataset
verification ladder T0 review T1 audit T2 compute T3 formal
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Existing work on sign language translation - that is, translation from sign language videos into sentences in a written language - has focused mainly on (1) data collected in a controlled environment or (2) data in a specific domain, which limits the applicability to real-world settings. In this paper, we introduce OpenASL, a large-scale American Sign Language (ASL) - English dataset collected from online video sites (e.g., YouTube). OpenASL contains 288 hours of ASL videos in multiple domains from over 200 signers and is the largest publicly available ASL translation dataset to date. To tackle the challenges of sign language translation in realistic settings and without glosses, we propose a set of techniques including sign search as a pretext task for pre-training and fusion of mouthing and handshape features. The proposed techniques produce consistent and large improvements in translation quality, over baseline models based on prior work. Our data and code are publicly available at https://github.com/chevalierNoir/OpenASL

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Cited by 4 Pith papers

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

  1. Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Isharah is a new 30,000-clip, multi-scene Saudi Sign Language dataset with gloss and translation annotations, plus signer-independent and unseen-sentence benchmarks for continuous sign language recognition and translation.

  2. Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    LLM-generated pseudo glosses, reordered via weak video supervision, enable sign language translation that rivals gloss-supervised models while needing only 30 gloss examples.

  3. EmoSign: A Multimodal Dataset for Understanding Emotions in American Sign Language

    cs.CV 2025-05 conditional novelty 6.0 of 10

    EmoSign is a 200-clip American Sign Language video dataset with native-signer sentiment and emotion labels plus baseline multimodal LLM results showing poor visual-only emotion recognition.

  4. Sign Spotting Disambiguation using Large Language Models

    cs.CV 2025-07 conditional novelty 5.0 of 10

    LLM-based beam search disambiguation improves dictionary sign spotting WER from 47.2% to 44.4% on an internal BSL dataset.

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