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    <titleInfo>
        <title>Semantic Neural Machine Translation Using AMR</title>
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    <name type="personal">
        <namePart type="given">Linfeng</namePart>
        <namePart type="family">Song</namePart>
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    <name type="personal">
        <namePart type="given">Zhiguo</namePart>
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    <name type="personal">
        <namePart type="given">Jinsong</namePart>
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    <abstract>It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.</abstract>
    <identifier type="citekey">song-etal-2019-semantic</identifier>
    <identifier type="doi">10.1162/tacl_a_00252</identifier>
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        <url>https://aclanthology.org/Q19-1002</url>
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    <part>
        <date>2019</date>
        <detail type="volume"><number>7</number></detail>
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            <start>19</start>
            <end>31</end>
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%0 Journal Article
%T Semantic Neural Machine Translation Using AMR
%A Song, Linfeng
%A Gildea, Daniel
%A Zhang, Yue
%A Wang, Zhiguo
%A Su, Jinsong
%J Transactions of the Association for Computational Linguistics
%D 2019
%V 7
%I MIT Press
%C Cambridge, MA
%F song-etal-2019-semantic
%X It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.
%R 10.1162/tacl_a_00252
%U https://aclanthology.org/Q19-1002
%U https://doi.org/10.1162/tacl_a_00252
%P 19-31

Markdown (Informal)

[Semantic Neural Machine Translation Using AMR](https://aclanthology.org/Q19-1002) (Song et al., TACL 2019)

• Semantic Neural Machine Translation Using AMR (Song et al., TACL 2019)

ACL

• Linfeng Song, Daniel Gildea, Yue Zhang, Zhiguo Wang, and Jinsong Su. 2019. Semantic Neural Machine Translation Using AMR. Transactions of the Association for Computational Linguistics, 7:19–31.