Add SEDA (Tan et al., 2024) to Video-to-Text section#159
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AmitMY
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Apr 28, 2026
| Using the same encoding, they use a transformer decoder to decode the spoken language text one token at a time. | ||
| They show that adding gloss supervision improves the model over not using it and that it outperforms previous video-to-gloss-to-text pipeline approaches [@cihan2018neural]. | ||
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| @tan-etal-2024-seda extend this sign language transformer with SEDA, a data augmentation framework that augments sign features through multiple sign embeddings and augments spoken text via lemmatization, achieving competitive WER, BLEU, and ROUGE on RWTH-PHOENIX-Weather-2014T. |
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should be the last paragarph in this section chronologically
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Done — moved to the end of the Video-to-Text section. |
Adds a sentence describing SEDA's sign-feature and spoken-text augmentation plus multi-task learning extension to the sign language transformer in the Video-to-Text section, and appends the corresponding BibTeX entry. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
tan-etal-2024-seda) tosrc/references.bib.Test plan
Paper: https://aclanthology.org/2024.signlang-1.41/
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