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        <title>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</title>
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    <abstract>State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language (SL) glosses. In our experiments, we improve the performance of the transformer-based models via (1) data augmentation, (2) semi-supervised Neural Machine Translation (NMT), (3) transfer learning and (4) multilingual NMT. The proposed methods are implemented progressively on two German SL corpora containing gloss annotations. Multilingual NMT combined with data augmentation appear to be the most successful setting, yielding statistically significant improvements as measured by three automatic metrics (up to over 6 points BLEU), and confirmed via human evaluation. Our best setting outperforms all previous work that report on the same test-set and is also confirmed on a corpus of the American Sign Language (ASL).</abstract>
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%0 Conference Proceedings
%T Neural Machine Translation Methods for Translating Text to Sign Language Glosses
%A Zhu, Dele
%A Czehmann, Vera
%A Avramidis, Eleftherios
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhu-etal-2023-neural
%X State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language (SL) glosses. In our experiments, we improve the performance of the transformer-based models via (1) data augmentation, (2) semi-supervised Neural Machine Translation (NMT), (3) transfer learning and (4) multilingual NMT. The proposed methods are implemented progressively on two German SL corpora containing gloss annotations. Multilingual NMT combined with data augmentation appear to be the most successful setting, yielding statistically significant improvements as measured by three automatic metrics (up to over 6 points BLEU), and confirmed via human evaluation. Our best setting outperforms all previous work that report on the same test-set and is also confirmed on a corpus of the American Sign Language (ASL).
%R 10.18653/v1/2023.acl-long.700
%U https://aclanthology.org/2023.acl-long.700
%U https://doi.org/10.18653/v1/2023.acl-long.700
%P 12523-12541

Markdown (Informal)

[Neural Machine Translation Methods for Translating Text to Sign Language Glosses](https://aclanthology.org/2023.acl-long.700) (Zhu et al., ACL 2023)

• Neural Machine Translation Methods for Translating Text to Sign Language Glosses (Zhu et al., ACL 2023)

ACL

• Dele Zhu, Vera Czehmann, and Eleftherios Avramidis. 2023. Neural Machine Translation Methods for Translating Text to Sign Language Glosses. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12523–12541, Toronto, Canada. Association for Computational Linguistics.