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        <title>ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs</title>
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        <namePart type="given">Wenpeng</namePart>
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        <namePart type="family">Xiang</namePart>
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    <abstract>How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence’s representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer_Selection.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00097</identifier>
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%0 Journal Article
%T ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
%A Yin, Wenpeng
%A Schütze, Hinrich
%A Xiang, Bing
%A Zhou, Bowen
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F yin-etal-2016-abcnn
%X How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence’s representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer_Selection.
%R 10.1162/tacl_a_00097
%U https://aclanthology.org/Q16-1019
%U https://doi.org/10.1162/tacl_a_00097
%P 259-272

Markdown (Informal)

[ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs](https://aclanthology.org/Q16-1019) (Yin et al., TACL 2016)

• ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs (Yin et al., TACL 2016)

ACL

• Wenpeng Yin, Hinrich Schütze, Bing Xiang, and Bowen Zhou. 2016. ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. Transactions of the Association for Computational Linguistics, 4:259–272.