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        <title>CNN for Text-Based Multiple Choice Question Answering</title>
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        <namePart type="given">Akshay</namePart>
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        <namePart type="given">Onkar</namePart>
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    <name type="personal">
        <namePart type="given">Utpal</namePart>
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        <dateIssued>2018-07</dateIssued>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets.</abstract>
    <identifier type="citekey">chaturvedi-etal-2018-cnn</identifier>
    <identifier type="doi">10.18653/v1/P18-2044</identifier>
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        <url>https://aclanthology.org/P18-2044</url>
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%0 Conference Proceedings
%T CNN for Text-Based Multiple Choice Question Answering
%A Chaturvedi, Akshay
%A Pandit, Onkar
%A Garain, Utpal
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F chaturvedi-etal-2018-cnn
%X The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets.
%R 10.18653/v1/P18-2044
%U https://aclanthology.org/P18-2044
%U https://doi.org/10.18653/v1/P18-2044
%P 272-277

Markdown (Informal)

[CNN for Text-Based Multiple Choice Question Answering](https://aclanthology.org/P18-2044) (Chaturvedi et al., ACL 2018)

• CNN for Text-Based Multiple Choice Question Answering (Chaturvedi et al., ACL 2018)

ACL

• Akshay Chaturvedi, Onkar Pandit, and Utpal Garain. 2018. CNN for Text-Based Multiple Choice Question Answering. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 272–277, Melbourne, Australia. Association for Computational Linguistics.