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        <title>Improving Neural Machine Translation through Phrase-based Forced Decoding</title>
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    <name type="personal">
        <namePart type="given">Jingyi</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">Eiichro</namePart>
        <namePart type="family">Sumita</namePart>
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    <name type="personal">
        <namePart type="given">Graham</namePart>
        <namePart type="family">Neubig</namePart>
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    <name type="personal">
        <namePart type="given">Satoshi</namePart>
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        <dateIssued>2017-11</dateIssued>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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            <namePart type="family">Kondrak</namePart>
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            <publisher>Asian Federation of Natural Language Processing</publisher>
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    <abstract>Compared to traditional statistical machine translation (SMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency. We propose a method to combine the advantages of traditional SMT and NMT by exploiting an existing phrase-based SMT model to compute the phrase-based decoding cost for an NMT output and then using the phrase-based decoding cost to rerank the n-best NMT outputs. The main challenge in implementing this approach is that NMT outputs may not be in the search space of the standard phrase-based decoding algorithm, because the search space of phrase-based SMT is limited by the phrase-based translation rule table. We propose a soft forced decoding algorithm, which can always successfully find a decoding path for any NMT output. We show that using the forced decoding cost to rerank the NMT outputs can successfully improve translation quality on four different language pairs.</abstract>
    <identifier type="citekey">zhang-etal-2017-improving</identifier>
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        <url>https://aclanthology.org/I17-1016</url>
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        <date>2017-11</date>
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            <start>152</start>
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%0 Conference Proceedings
%T Improving Neural Machine Translation through Phrase-based Forced Decoding
%A Zhang, Jingyi
%A Utiyama, Masao
%A Sumita, Eiichro
%A Neubig, Graham
%A Nakamura, Satoshi
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F zhang-etal-2017-improving
%X Compared to traditional statistical machine translation (SMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency. We propose a method to combine the advantages of traditional SMT and NMT by exploiting an existing phrase-based SMT model to compute the phrase-based decoding cost for an NMT output and then using the phrase-based decoding cost to rerank the n-best NMT outputs. The main challenge in implementing this approach is that NMT outputs may not be in the search space of the standard phrase-based decoding algorithm, because the search space of phrase-based SMT is limited by the phrase-based translation rule table. We propose a soft forced decoding algorithm, which can always successfully find a decoding path for any NMT output. We show that using the forced decoding cost to rerank the NMT outputs can successfully improve translation quality on four different language pairs.
%U https://aclanthology.org/I17-1016
%P 152-162

Markdown (Informal)

[Improving Neural Machine Translation through Phrase-based Forced Decoding](https://aclanthology.org/I17-1016) (Zhang et al., IJCNLP 2017)

• Improving Neural Machine Translation through Phrase-based Forced Decoding (Zhang et al., IJCNLP 2017)

ACL

• Jingyi Zhang, Masao Utiyama, Eiichro Sumita, Graham Neubig, and Satoshi Nakamura. 2017. Improving Neural Machine Translation through Phrase-based Forced Decoding. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 152–162, Taipei, Taiwan. Asian Federation of Natural Language Processing.