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        <title>Dependency-Based Relative Positional Encoding for Transformer NMT</title>
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        <namePart type="family">Tamura</namePart>
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    <name type="personal">
        <namePart type="given">Takashi</namePart>
        <namePart type="family">Ninomiya</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism. In particular, the proposed model encodes pair-wise relative depths on a source dependency tree, which are differences between the depths of the two source words, in the encoder’s self-attention. The experiments show that our proposed model achieves 0.5 point gain in BLEU on the Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_099</identifier>
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%0 Conference Proceedings
%T Dependency-Based Relative Positional Encoding for Transformer NMT
%A Omote, Yutaro
%A Tamura, Akihiro
%A Ninomiya, Takashi
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F omote-etal-2019-dependency
%X This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism. In particular, the proposed model encodes pair-wise relative depths on a source dependency tree, which are differences between the depths of the two source words, in the encoder’s self-attention. The experiments show that our proposed model achieves 0.5 point gain in BLEU on the Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.
%R 10.26615/978-954-452-056-4_099
%U https://aclanthology.org/R19-1099
%U https://doi.org/10.26615/978-954-452-056-4_099
%P 854-861

Markdown (Informal)

[Dependency-Based Relative Positional Encoding for Transformer NMT](https://aclanthology.org/R19-1099) (Omote et al., RANLP 2019)

• Dependency-Based Relative Positional Encoding for Transformer NMT (Omote et al., RANLP 2019)

ACL

• Yutaro Omote, Akihiro Tamura, and Takashi Ninomiya. 2019. Dependency-Based Relative Positional Encoding for Transformer NMT. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 854–861, Varna, Bulgaria. INCOMA Ltd..