Reconsidering Sentence-Level Sign Language Translation

• URL: http://arxiv.org/abs/2406.11049v1
• Date: Sun, 16 Jun 2024 19:19:54 GMT
• Title: Reconsidering Sentence-Level Sign Language Translation
• Authors: Garrett Tanzer, Maximus Shengelia, Ken Harrenstien, David Uthus,
• Abstract summary: We show that for 33% of sentences in our sample, our fluent Deaf signer annotators were only able to understand key parts of the clip in light of discourse-level context. These results underscore the importance of understanding and sanity checking examples when adapting machine learning to new domains.
• Score: 2.099922236065961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Historically, sign language machine translation has been posed as a sentence-level task: datasets consisting of continuous narratives are chopped up and presented to the model as isolated clips. In this work, we explore the limitations of this task framing. First, we survey a number of linguistic phenomena in sign languages that depend on discourse-level context. Then as a case study, we perform the first human baseline for sign language translation that actually substitutes a human into the machine learning task framing, rather than provide the human with the entire document as context. This human baseline -- for ASL to English translation on the How2Sign dataset -- shows that for 33% of sentences in our sample, our fluent Deaf signer annotators were only able to understand key parts of the clip in light of additional discourse-level context. These results underscore the importance of understanding and sanity checking examples when adapting machine learning to new domains.

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