POSESTITCH-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911244206735360 |
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| author | Joshi, Abhinav Sharma, Vaibhav Singh, Sanjeet Modi, Ashutosh |
| author_facet | Joshi, Abhinav Sharma, Vaibhav Singh, Sanjeet Modi, Ashutosh |
| contents | Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. Prior arts have focused on various feature extraction and architectural changes to support neural machine translation for sign languages. We propose POSESTITCH-SLT, a novel pre-training scheme that is inspired by linguistic-templates-based sentence generation technique. With translation comparison on two sign language datasets, How2Sign and iSign, we show that a simple transformer-based encoder-decoder architecture outperforms the prior art when considering template-generated sentence pairs in training. We achieve BLEU-4 score improvements from 1.97 to 4.56 on How2Sign and from 0.55 to 3.43 on iSign, surpassing prior state-of-the-art methods for pose-based gloss-free translation. The results demonstrate the effectiveness of template-driven synthetic supervision in low-resource sign language settings. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_00270 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | POSESTITCH-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation Joshi, Abhinav Sharma, Vaibhav Singh, Sanjeet Modi, Ashutosh Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. Prior arts have focused on various feature extraction and architectural changes to support neural machine translation for sign languages. We propose POSESTITCH-SLT, a novel pre-training scheme that is inspired by linguistic-templates-based sentence generation technique. With translation comparison on two sign language datasets, How2Sign and iSign, we show that a simple transformer-based encoder-decoder architecture outperforms the prior art when considering template-generated sentence pairs in training. We achieve BLEU-4 score improvements from 1.97 to 4.56 on How2Sign and from 0.55 to 3.43 on iSign, surpassing prior state-of-the-art methods for pose-based gloss-free translation. The results demonstrate the effectiveness of template-driven synthetic supervision in low-resource sign language settings. |
| title | POSESTITCH-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2511.00270 |