Papers by Garrett Tanzer
FLEURS-ASL: Including American Sign Language in Massively Multilingual Multitask Evaluation (2025.naacl-long)
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| Challenge: | Sign language translation has traditionally been peripheral to mainstream machine translation research. |
| Approach: | They propose a sign language benchmark extension that supports their first sign language, American Sign Language . they provide baselines for tasks from ASL to English text using a unified modeling approach . |
| Outcome: | The proposed model exceeds phrase-level benchmarks while supporting new tasks. |
Fingerspelling within Sign Language Translation (2025.naacl-long)
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| Challenge: | Prior work has studied fingerspelling recognition, but little attention has been paid to how well models understand it in context of entire sentences. |
| Approach: | They annotate instances of fingerspelling within FLEURS-ASL and use it to evaluate how well translation models understand it. |
| Outcome: | The proposed model family significantly improves understanding of fingerspelling, but the effect of the mixed model is mixed. |
Reconsidering Sentence-Level Sign Language Translation (2024.emnlp-main)
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| Challenge: | Historically, sign language machine translation is framed as a sentence-level task . however, there are known intersentential dependencies that are impossible to resolve in isolation. |
| Approach: | They propose a human baseline for sign language translation that substitutes a person into the machine learning task framing instead of providing the entire document as context. |
| Outcome: | The proposed human baseline for sign language translation shows that deaf signers can only understand key parts of the clip in light of additional discourse-level context. |