Non-Autoregressive Sign Language Translation Technology Based on Transformer and Multimodal Alignment

SHAO Shuyu; DU Yao; FAN Xiaoli

doi:10.11999/JEIT230801

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SHAO Shuyu, DU Yao, FAN Xiaoli. Non-Autoregressive Sign Language Translation Technology Based on Transformer and Multimodal Alignment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230801

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SHAO Shuyu, DU Yao, FAN Xiaoli. Non-Autoregressive Sign Language Translation Technology Based on Transformer and Multimodal Alignment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230801

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Non-Autoregressive Sign Language Translation Technology Based on Transformer and Multimodal Alignment

doi: 10.11999/JEIT230801

SHAO Shuyu^{1
,
,},
DU Yao²,
FAN Xiaoli³

1.
School of Logistics, Beijing Wuzi University, Beijing 101149, China
2.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
3.
Air force medical center, PLA, Beijing 101142, China

Funds: The National Natural Science Foundation of China (8210072143), R&D Program of Beijing Municipal Education Commission (KM202210037001)

Received Date: 2023-08-01
Rev Recd Date: 2023-12-27

Available Online: 2024-01-08

Abstract

Abstract

To address the challenge of aligning multimodal data and improving the slow translation speed in sign language translation, a Transformer Sign Language Translation Non-Autoregression (Trans-SLT-NA) is proposed in this paper, which utilizes a self-attention mechanism. Additionally, it incorporates a contrastive learning loss function to align the multimodal data. By capturing the contextual and interaction information between the input sequence (sign language videos) and the target sequence (text), the proposed model is able to perform sign language translation to natural language in s single step. The effectiveness of the proposed model is evaluated on publicly available datasets, including PHOENIX-2014-T (German), CSL (Chinese) and How2Sign (English). Results demonstrate that the proposed method achieves a significant improvement in translation speed, with a speed boost ranging from 11.6 to 17.6 times compared to autoregressive models, while maintaining comparable performance in terms of BiLingual Evaluation Understudy (BLEU-4) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics.
- Sign language translation,
- Self-attention mechanism,
- Non-autoregressive translation,
- Deep learning,
- Alignment of multimodal data

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References(27)

References

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