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Neural Sign Language Translation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add gloss-level intermediate supervision to direct video-to-text training | Sign language translation · PHOENIX14T test | BLEU-4: 9.58 -> 18.13 | Helped |
| Train the translation network on predicted glosses rather than applying a ground-truth-gloss-trained model | S2G2T · PHOENIX14T test | BLEU-4: 17.79 -> 18.13 | Helped |
| Use GRU units instead of LSTM units (ablation) | G2T · PHOENIX14T test | BLEU-4: 16.58 -> 18.75 | Helped |
| Add attention instead of using no attention (ablation) | G2T · PHOENIX14T test | BLEU-4: 16.34 -> 18.75 | Helped |
| Use Luong attention instead of Bahdanau attention (ablation) | G2T · PHOENIX14T test | BLEU-4: 17.40 -> 18.75 | Helped |
| Use batch size 1 instead of batch size 128 (ablation) | G2T · PHOENIX14T test | BLEU-4: 18.75 -> 19.26 | Helped |
| Add attention instead of using no attention (ablation) | S2T · PHOENIX14T test | BLEU-4: 8.35 -> 9.58 | Helped |
| Use Bahdanau attention instead of Luong attention (ablation) | S2T · PHOENIX14T test | BLEU-4: 9.00 -> 9.58 | Helped |
Closest papers by meaning
- Scaling Sign Language Translation (NeurIPS 2024 · similarity 0.94)
- Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation (NeurIPS 2025 · similarity 0.91)
- LLMs are Good Sign Language Translators (CVPR 2024 · similarity 0.91)
- Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues (CVPR 2025 · similarity 0.91)
- Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment (NeurIPS 2026 · similarity 0.91)
- Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation (ICLR 2024 · similarity 0.91)
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