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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yue</namePart>
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    <abstract>Simultaneous translation (ST) outputs translation while receiving the source inputs, and hence requires a policy to determine whether to translate a target token or wait for the next source token. The major challenge of ST is that each target token can only be translated based on the current received source tokens, where the received source information will directly affect the translation quality. So naturally, how much source information is received for the translation of the current target token is supposed to be the pivotal evidence for the ST policy to decide between translating and waiting. In this paper, we treat the translation as information transport from source to target and accordingly propose an Information-Transport-based Simultaneous Translation (ITST). ITST quantifies the transported information weight from each source token to the current target token, and then decides whether to translate the target token according to its accumulated received information. Experiments on both text-to-text ST and speech-to-text ST (a.k.a., streaming speech translation) tasks show that ITST outperforms strong baselines and achieves state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Information-Transport-based Policy for Simultaneous Translation
%A Zhang, Shaolei
%A Feng, Yang
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhang-feng-2022-information
%X Simultaneous translation (ST) outputs translation while receiving the source inputs, and hence requires a policy to determine whether to translate a target token or wait for the next source token. The major challenge of ST is that each target token can only be translated based on the current received source tokens, where the received source information will directly affect the translation quality. So naturally, how much source information is received for the translation of the current target token is supposed to be the pivotal evidence for the ST policy to decide between translating and waiting. In this paper, we treat the translation as information transport from source to target and accordingly propose an Information-Transport-based Simultaneous Translation (ITST). ITST quantifies the transported information weight from each source token to the current target token, and then decides whether to translate the target token according to its accumulated received information. Experiments on both text-to-text ST and speech-to-text ST (a.k.a., streaming speech translation) tasks show that ITST outperforms strong baselines and achieves state-of-the-art performance.
%R 10.18653/v1/2022.emnlp-main.65
%U https://aclanthology.org/2022.emnlp-main.65
%U https://doi.org/10.18653/v1/2022.emnlp-main.65
%P 992-1013

Markdown (Informal)

[Information-Transport-based Policy for Simultaneous Translation](https://aclanthology.org/2022.emnlp-main.65) (Zhang & Feng, EMNLP 2022)

• Information-Transport-based Policy for Simultaneous Translation (Zhang & Feng, EMNLP 2022)

ACL

• Shaolei Zhang and Yang Feng. 2022. Information-Transport-based Policy for Simultaneous Translation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 992–1013, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.