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    <name type="personal">
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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            <namePart type="given">Greg</namePart>
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    <abstract>This paper proposes a new attention mechanism for neural machine translation (NMT) based on convolutional neural networks (CNNs), which is inspired by the CKY algorithm. The proposed attention represents every possible combination of source words (e.g., phrases and structures) through CNNs, which imitates the CKY table in the algorithm. NMT, incorporating the proposed attention, decodes a target sentence on the basis of the attention scores of the hidden states of CNNs. The proposed attention enables NMT to capture alignments from underlying structures of a source sentence without sentence parsing. The evaluations on the Asian Scientific Paper Excerpt Corpus (ASPEC) English-Japanese translation task show that the proposed attention gains 0.66 points in BLEU.</abstract>
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%0 Conference Proceedings
%T CKY-based Convolutional Attention for Neural Machine Translation
%A Watanabe, Taiki
%A Tamura, Akihiro
%A Ninomiya, Takashi
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F watanabe-etal-2017-cky
%X This paper proposes a new attention mechanism for neural machine translation (NMT) based on convolutional neural networks (CNNs), which is inspired by the CKY algorithm. The proposed attention represents every possible combination of source words (e.g., phrases and structures) through CNNs, which imitates the CKY table in the algorithm. NMT, incorporating the proposed attention, decodes a target sentence on the basis of the attention scores of the hidden states of CNNs. The proposed attention enables NMT to capture alignments from underlying structures of a source sentence without sentence parsing. The evaluations on the Asian Scientific Paper Excerpt Corpus (ASPEC) English-Japanese translation task show that the proposed attention gains 0.66 points in BLEU.
%U https://aclanthology.org/I17-2001
%P 1-6

Markdown (Informal)

[CKY-based Convolutional Attention for Neural Machine Translation](https://aclanthology.org/I17-2001) (Watanabe et al., IJCNLP 2017)

• CKY-based Convolutional Attention for Neural Machine Translation (Watanabe et al., IJCNLP 2017)

ACL

• Taiki Watanabe, Akihiro Tamura, and Takashi Ninomiya. 2017. CKY-based Convolutional Attention for Neural Machine Translation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 1–6, Taipei, Taiwan. Asian Federation of Natural Language Processing.