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    <abstract>Simultaneous machine translation (SiMT) outputs translation while reading source sentence and hence requires a policy to decide whether to wait for the next source word (READ) or generate a target word (WRITE), the actions of which form a read/write path. Although the read/write path is essential to SiMT performance, no direct supervision is given to the path in the existing methods. In this paper, we propose a method of dual-path SiMT which introduces duality constraints to direct the read/write path. According to duality constraints, the read/write path in source-to-target and target-to-source SiMT models can be mapped to each other. As a result, the two SiMT models can be optimized jointly by forcing their read/write paths to satisfy the mapping. Experiments on En-Vi and De-En tasks show that our method can outperform strong baselines under all latency.</abstract>
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%0 Conference Proceedings
%T Modeling Dual Read/Write Paths for Simultaneous Machine Translation
%A Zhang, Shaolei
%A Feng, Yang
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhang-feng-2022-modeling
%X Simultaneous machine translation (SiMT) outputs translation while reading source sentence and hence requires a policy to decide whether to wait for the next source word (READ) or generate a target word (WRITE), the actions of which form a read/write path. Although the read/write path is essential to SiMT performance, no direct supervision is given to the path in the existing methods. In this paper, we propose a method of dual-path SiMT which introduces duality constraints to direct the read/write path. According to duality constraints, the read/write path in source-to-target and target-to-source SiMT models can be mapped to each other. As a result, the two SiMT models can be optimized jointly by forcing their read/write paths to satisfy the mapping. Experiments on En-Vi and De-En tasks show that our method can outperform strong baselines under all latency.
%R 10.18653/v1/2022.acl-long.176
%U https://aclanthology.org/2022.acl-long.176
%U https://doi.org/10.18653/v1/2022.acl-long.176
%P 2461-2477

Markdown (Informal)

[Modeling Dual Read/Write Paths for Simultaneous Machine Translation](https://aclanthology.org/2022.acl-long.176) (Zhang & Feng, ACL 2022)

• Modeling Dual Read/Write Paths for Simultaneous Machine Translation (Zhang & Feng, ACL 2022)

ACL

• Shaolei Zhang and Yang Feng. 2022. Modeling Dual Read/Write Paths for Simultaneous Machine Translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2461–2477, Dublin, Ireland. Association for Computational Linguistics.