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        <title>Continual Learning in Multilingual Sign Language Translation</title>
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        <namePart type="given">Shakib</namePart>
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            <title>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</title>
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            <namePart type="given">Luis</namePart>
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    <abstract>The field of sign language translation (SLT) is still in its infancy, as evidenced by the low translation quality, even when using deep learn- ing approaches. Probably because of this, many common approaches in other machine learning fields have not been explored in sign language. Here, we focus on continual learning for mul- tilingual SLT. We experiment with three con- tinual learning methods and compare them to four more naive baseline and fine-tuning ap- proaches. We work with four sign languages (ASL, BSL, CSL and DGS) and three spo- ken languages (Chinese, English and German). Our results show that incremental fine-tuning is the best performing approach both in terms of translation quality and transfer capabilities, and that continual learning approaches are not yet fully competitive given the current SOTA in SLT.</abstract>
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%0 Conference Proceedings
%T Continual Learning in Multilingual Sign Language Translation
%A Yazdani, Shakib
%A Genabith, Josef Van
%A España-Bonet, Cristina
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F yazdani-etal-2025-continual
%X The field of sign language translation (SLT) is still in its infancy, as evidenced by the low translation quality, even when using deep learn- ing approaches. Probably because of this, many common approaches in other machine learning fields have not been explored in sign language. Here, we focus on continual learning for mul- tilingual SLT. We experiment with three con- tinual learning methods and compare them to four more naive baseline and fine-tuning ap- proaches. We work with four sign languages (ASL, BSL, CSL and DGS) and three spo- ken languages (Chinese, English and German). Our results show that incremental fine-tuning is the best performing approach both in terms of translation quality and transfer capabilities, and that continual learning approaches are not yet fully competitive given the current SOTA in SLT.
%R 10.18653/v1/2025.naacl-long.546
%U https://aclanthology.org/2025.naacl-long.546/
%U https://doi.org/10.18653/v1/2025.naacl-long.546
%P 10923-10938

Markdown (Informal)

[Continual Learning in Multilingual Sign Language Translation](https://aclanthology.org/2025.naacl-long.546/) (Yazdani et al., NAACL 2025)

• Continual Learning in Multilingual Sign Language Translation (Yazdani et al., NAACL 2025)

ACL

• Shakib Yazdani, Josef Van Genabith, and Cristina España-Bonet. 2025. Continual Learning in Multilingual Sign Language Translation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 10923–10938, Albuquerque, New Mexico. Association for Computational Linguistics.