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        <title>Neural Machine Translation with Reordering Embeddings</title>
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        <namePart type="given">Kehai</namePart>
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    <name type="personal">
        <namePart type="given">Masao</namePart>
        <namePart type="family">Utiyama</namePart>
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    <name type="personal">
        <namePart type="given">Eiichiro</namePart>
        <namePart type="family">Sumita</namePart>
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    <abstract>The reordering model plays an important role in phrase-based statistical machine translation. However, there are few works that exploit the reordering information in neural machine translation. In this paper, we propose a reordering mechanism to learn the reordering embedding of a word based on its contextual information. These learned reordering embeddings are stacked together with self-attention networks to learn sentence representation for machine translation. The reordering mechanism can be easily integrated into both the encoder and the decoder in the Transformer translation system. Experimental results on WMT’14 English-to-German, NIST Chinese-to-English, and WAT Japanese-to-English translation tasks demonstrate that the proposed methods can significantly improve the performance of the Transformer.</abstract>
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%0 Conference Proceedings
%T Neural Machine Translation with Reordering Embeddings
%A Chen, Kehai
%A Wang, Rui
%A Utiyama, Masao
%A Sumita, Eiichiro
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-etal-2019-neural
%X The reordering model plays an important role in phrase-based statistical machine translation. However, there are few works that exploit the reordering information in neural machine translation. In this paper, we propose a reordering mechanism to learn the reordering embedding of a word based on its contextual information. These learned reordering embeddings are stacked together with self-attention networks to learn sentence representation for machine translation. The reordering mechanism can be easily integrated into both the encoder and the decoder in the Transformer translation system. Experimental results on WMT’14 English-to-German, NIST Chinese-to-English, and WAT Japanese-to-English translation tasks demonstrate that the proposed methods can significantly improve the performance of the Transformer.
%R 10.18653/v1/P19-1174
%U https://aclanthology.org/P19-1174
%U https://doi.org/10.18653/v1/P19-1174
%P 1787-1799

Markdown (Informal)

[Neural Machine Translation with Reordering Embeddings](https://aclanthology.org/P19-1174) (Chen et al., ACL 2019)

• Neural Machine Translation with Reordering Embeddings (Chen et al., ACL 2019)

ACL

• Kehai Chen, Rui Wang, Masao Utiyama, and Eiichiro Sumita. 2019. Neural Machine Translation with Reordering Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1787–1799, Florence, Italy. Association for Computational Linguistics.