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        <title>Improving Neural Machine Translation with Soft Template Prediction</title>
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    <name type="personal">
        <namePart type="given">Jian</namePart>
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        <namePart type="given">Shuming</namePart>
        <namePart type="family">Ma</namePart>
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    <name type="personal">
        <namePart type="given">Dongdong</namePart>
        <namePart type="family">Zhang</namePart>
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        <namePart type="given">Zhoujun</namePart>
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    <name type="personal">
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Although neural machine translation (NMT) has achieved significant progress in recent years, most previous NMT models only depend on the source text to generate translation. Inspired by the success of template-based and syntax-based approaches in other fields, we propose to use extracted templates from tree structures as soft target templates to guide the translation procedure. In order to learn the syntactic structure of the target sentences, we adopt constituency-based parse tree to generate candidate templates. We incorporate the template information into the encoder-decoder framework to jointly utilize the templates and source text. Experiments show that our model significantly outperforms the baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.</abstract>
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        <date>2020-07</date>
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            <start>5979</start>
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%0 Conference Proceedings
%T Improving Neural Machine Translation with Soft Template Prediction
%A Yang, Jian
%A Ma, Shuming
%A Zhang, Dongdong
%A Li, Zhoujun
%A Zhou, Ming
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yang-etal-2020-improving-neural
%X Although neural machine translation (NMT) has achieved significant progress in recent years, most previous NMT models only depend on the source text to generate translation. Inspired by the success of template-based and syntax-based approaches in other fields, we propose to use extracted templates from tree structures as soft target templates to guide the translation procedure. In order to learn the syntactic structure of the target sentences, we adopt constituency-based parse tree to generate candidate templates. We incorporate the template information into the encoder-decoder framework to jointly utilize the templates and source text. Experiments show that our model significantly outperforms the baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.
%R 10.18653/v1/2020.acl-main.531
%U https://aclanthology.org/2020.acl-main.531
%U https://doi.org/10.18653/v1/2020.acl-main.531
%P 5979-5989

Markdown (Informal)

[Improving Neural Machine Translation with Soft Template Prediction](https://aclanthology.org/2020.acl-main.531) (Yang et al., ACL 2020)

• Improving Neural Machine Translation with Soft Template Prediction (Yang et al., ACL 2020)

ACL

• Jian Yang, Shuming Ma, Dongdong Zhang, Zhoujun Li, and Ming Zhou. 2020. Improving Neural Machine Translation with Soft Template Prediction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5979–5989, Online. Association for Computational Linguistics.