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Improving Gloss-free Sign Language Translation by Reducing Representation Density
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Identifies a representation density problem in gloss-free sign language translation and proposes SignCL, a self-supervised contrastive loss on frame features that pulls adjacent frames together and pushes distant frames apart, improving GFSLT-VLP and Sign Language Transformer on PHOENIX-2014T and CSL-Daily.
Problem
Gloss-free sign language translation lags far behind gloss-based methods because visual representations of semantically distinct sign gestures are densely clustered in feature space, making them hard to distinguish.
Approach
Introduce a Sign Density Ratio metric to quantify representation density and a temporal contrastive loss (SignCL) that treats nearby frames as positives and far-apart frames as negatives, with a margin derived from video length and text length, added to the VLP pretraining and/or SLT finetuning loss without extra parameters.
Key results
- On CSL-Daily, adding SignCL to GFSLT-VLP raises BLEU-4 from 11.10 to 16.16 (+5.06, about 46%) while reducing SDR from 58.20 to 54.61.
- On PHOENIX-2014T, SignCL in both pretraining and finetuning lifts GFSLT-VLP BLEU-4 from 21.25 to 22.74 and reduces SDR from 68.53 to 62.32.
- SignCL improves the Sign Language Transformer on CSL-Daily BLEU-4 from 1.82 to 2.53 for VLP features, and the paper reports that SignCL with 600M parameters beats Sign2GPT (about 1.7B) on PHOENIX-2014T.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add SignCL during pretraining and finetuning | GFSLT-VLP · CSL-Daily test | B@4: 11.10 -> 16.16 | Helped |
| Add SignCL during pretraining and finetuning | GFSLT-VLP · PHOENIX-2014T test | B@4: 21.25 -> 22.74 | Helped |
| Add SignCL to feature extraction and finetuning | Sign Language Transformer · PHOENIX-2014T | B@4: 10.73 -> 13.51 | Helped |
| Add SignCL to feature extraction and finetuning | Sign Language Transformer · CSL-Daily | B@4: 1.82 -> 2.53 | Helped |
| Apply SignCL during pretraining only (ablation) | GFSLT-VLP · PHOENIX-2014T test | B@4: 21.25 -> 22.69 | Helped |
| Apply SignCL during finetuning only (ablation) | GFSLT-VLP · PHOENIX-2014T test | B@4: 21.25 -> 22.16 | Helped |
| Apply SignCL during pretraining only (ablation) | GFSLT-VLP · CSL-Daily test | B@4: 11.10 -> 15.85 | Helped |
| Apply SignCL during finetuning only (ablation) | GFSLT-VLP · CSL-Daily test | B@4: 11.10 -> 15.98 | Helped |
| Vary the dynamic margin threshold (ablation) | SignCL · PHOENIX-2014T | B@4: 17.24; 17.63; 17.55; 17.63; 17.13; 17.11 | No clear effect |
| Vary the Zipf factor (ablation) | SignCL · PHOENIX-2014T | B@4: 17.45; 17.89; 17.63; 17.29; 16.26 | Mixed |
| Increase the SignCL loss weight excessively (ablation) | GFSLT-VLP · PHOENIX-2014T | translation performance | Hurt |
Surprising findings
- Gloss-free features (VLP) show much higher representation density than gloss-based features (SDR 92.59 vs 66.23 on PHOENIX-2014T).
- SignCL applied only during finetuning leaves the SDR unchanged on PHOENIX-2014T (66.23 to 66.23 for the gloss-based Joint-SLT setting) while still improving BLEU slightly.
Limitations
- Assumes adjacent frames share a gesture and distant frames differ, which may fail on videos with repetitive gestures.
- Does not account for semantic similarity between gestures, so semantically similar signs may be pushed apart.
- Error bars are not reported due to computational cost, and the margin/threshold analyses use a reduced 80-epoch training schedule.
Experimental setup
Datasets: PHOENIX-2014T CSL-Daily
Benchmarks: PHOENIX-2014T CSL-Daily
Metrics: BLEU-4 BLEU-1 BLEU-2 BLEU-3 ROUGE-L WER Sign Density Ratio recognition accuracy
Baselines: NSLT GASLT GFSLT GFSLT-VLP Sign2GPT Joint-SLT Self-Mutual KD I3D Pretraining
Builds on: GFSLT-VLP Sign Language Transformer Joint-SLT CLIP mBART
Closest papers by meaning
- Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation (ICLR 2024 · similarity 0.93)
- Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation (NeurIPS 2025 · similarity 0.93)
- LLMs are Good Sign Language Translators (CVPR 2024 · similarity 0.90)
- Scaling Sign Language Translation (NeurIPS 2024 · similarity 0.90)
- Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment (NeurIPS 2026 · similarity 0.90)
- Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment (AAAI 2024 · similarity 0.89)
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