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| Asıl Yazarlar: | , , |
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| Materyal Türü: | Recurso digital |
| Dil: | |
| Baskı/Yayın Bilgisi: |
Zenodo
2024
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| Konular: | |
| Online Erişim: | https://doi.org/10.5281/zenodo.15344009 |
| Etiketler: |
Etiketle
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İçindekiler:
- <p>Word2Vec-based pre-trained word or gloss embedding models for Sign Languages. These are created by modifying the weights of pre-existing spoken language word embedding models to their SL counterparts, owing to their shared lexemes in Sign Language gloss. A full description of how they are created may be found in the companion paper (McGill et al., 2024).</p> <p>Languages included:</p> <ul> <li>American Sign Language (ASL) <ul> <li>3M embeddings, 5.1k unique signs, 300D</li> </ul> </li> <li>Spanish Sign Language (LSE) <ul> <li>1M embeddings, 1.2k unique signs, 300D</li> </ul> </li> <li>Sign Language of the Netherlands (NGT) <ul> <li>627k embeddings, 4.1k unique signs, 320D</li> </ul> </li> <li>Finnish Sign Language (FinSL) <ul> <li>247k embeddings, 3.1k unique signs, 100D</li> </ul> </li> </ul> <p>All four models are in .txt format, with the binary versions available for ASL and LSE. Don't hesitate to contact for further information.</p> <p> </p> <p>Abstract from original paper:</p> <p>This paper explores a novel method to modify existing pre-trained word embedding models of spoken languages for Sign Language glosses. These newly-generated embeddings are described, visualised, and then used in the encoder and/or decoder of models for the Text2Gloss and Gloss2Text task of machine translation. In two translation settings (one including data augmentation-based pre-training and a baseline), we find that bootstrapped word embeddings for glosses improve translation across four Signed/spoken language pairs. Many improvements are statistically significant, including those where the bootstrapped gloss embedding models are used.</p> <p>Languages included: American Sign Language, Finnish Sign Language, Spanish Sign Language, Sign Language of The Netherlands.</p>