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        <title>Robust Neural Machine Translation with Joint Textual and Phonetic Embedding</title>
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    <name type="personal">
        <namePart type="given">Hairong</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Zhongjun</namePart>
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    <abstract>Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. We propose to improve the robustness of NMT to homophone noises by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noises. Interestingly, to achieve better translation quality and more robustness, we found that most (though not all) weights should be put on the phonetic rather than textual information. Experiments show that our method not only significantly improves the robustness of NMT to homophone noises, but also surprisingly improves the translation quality on some clean test sets.</abstract>
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%0 Conference Proceedings
%T Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
%A Liu, Hairong
%A Ma, Mingbo
%A Huang, Liang
%A Xiong, Hao
%A He, Zhongjun
%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 liu-etal-2019-robust
%X Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. We propose to improve the robustness of NMT to homophone noises by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noises. Interestingly, to achieve better translation quality and more robustness, we found that most (though not all) weights should be put on the phonetic rather than textual information. Experiments show that our method not only significantly improves the robustness of NMT to homophone noises, but also surprisingly improves the translation quality on some clean test sets.
%R 10.18653/v1/P19-1291
%U https://aclanthology.org/P19-1291
%U https://doi.org/10.18653/v1/P19-1291
%P 3044-3049

Markdown (Informal)

[Robust Neural Machine Translation with Joint Textual and Phonetic Embedding](https://aclanthology.org/P19-1291) (Liu et al., ACL 2019)

• Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (Liu et al., ACL 2019)

ACL

• Hairong Liu, Mingbo Ma, Liang Huang, Hao Xiong, and Zhongjun He. 2019. Robust Neural Machine Translation with Joint Textual and Phonetic Embedding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3044–3049, Florence, Italy. Association for Computational Linguistics.