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        <title>WACO: Word-Aligned Contrastive Learning for Speech Translation</title>
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        <namePart type="given">Siqi</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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    <abstract>End-to-end Speech Translation (E2E ST) aims to directly translate source speech into target text. Existing ST methods perform poorly when only extremely small speech-text data are available for training. We observe that an ST model’s performance closely correlates with its embedding similarity between speech and source transcript. In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation. Our key idea is bridging word-level representations for both speech and text modalities via contrastive learning. We evaluate WACO and other methods on the MuST-C dataset, a widely used ST benchmark, and on a low-resource direction Maltese-English from IWSLT 2023. Our experiments demonstrate that WACO outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. Code is available at https://github.com/owaski/WACO.</abstract>
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%0 Conference Proceedings
%T WACO: Word-Aligned Contrastive Learning for Speech Translation
%A Ouyang, Siqi
%A Ye, Rong
%A Li, Lei
%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 ouyang-etal-2023-waco
%X End-to-end Speech Translation (E2E ST) aims to directly translate source speech into target text. Existing ST methods perform poorly when only extremely small speech-text data are available for training. We observe that an ST model’s performance closely correlates with its embedding similarity between speech and source transcript. In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation. Our key idea is bridging word-level representations for both speech and text modalities via contrastive learning. We evaluate WACO and other methods on the MuST-C dataset, a widely used ST benchmark, and on a low-resource direction Maltese-English from IWSLT 2023. Our experiments demonstrate that WACO outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. Code is available at https://github.com/owaski/WACO.
%R 10.18653/v1/2023.acl-long.216
%U https://aclanthology.org/2023.acl-long.216
%U https://doi.org/10.18653/v1/2023.acl-long.216
%P 3891-3907

Markdown (Informal)

[WACO: Word-Aligned Contrastive Learning for Speech Translation](https://aclanthology.org/2023.acl-long.216) (Ouyang et al., ACL 2023)

• WACO: Word-Aligned Contrastive Learning for Speech Translation (Ouyang et al., ACL 2023)

ACL

• Siqi Ouyang, Rong Ye, and Lei Li. 2023. WACO: Word-Aligned Contrastive Learning for Speech Translation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3891–3907, Toronto, Canada. Association for Computational Linguistics.