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        <title>MC\²: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension</title>
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    <name type="personal">
        <namePart type="given">Xuanyu</namePart>
        <namePart type="family">Zhang</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">David</namePart>
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    <abstract>Conversational machine reading comprehension (CMRC) extends traditional single-turn machine reading comprehension (MRC) by multi-turn interactions, which requires machines to consider the history of conversation. Most of models simply combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. To comprehend context profoundly and efficiently from different perspectives, we propose a novel neural network model, Multi-perspective Convolutional Cube (MC\²). We regard each conversation as a cube. 1D and 2D convolutions are integrated with RNN in our model. To avoid models previewing the next turn of conversation, we also extend causal convolution partially to 2D. Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves state-of-the-art results.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1622</identifier>
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%0 Conference Proceedings
%T MC\²: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension
%A Zhang, Xuanyu
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhang-2019-mc
%X Conversational machine reading comprehension (CMRC) extends traditional single-turn machine reading comprehension (MRC) by multi-turn interactions, which requires machines to consider the history of conversation. Most of models simply combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. To comprehend context profoundly and efficiently from different perspectives, we propose a novel neural network model, Multi-perspective Convolutional Cube (MC\²). We regard each conversation as a cube. 1D and 2D convolutions are integrated with RNN in our model. To avoid models previewing the next turn of conversation, we also extend causal convolution partially to 2D. Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves state-of-the-art results.
%R 10.18653/v1/P19-1622
%U https://aclanthology.org/P19-1622
%U https://doi.org/10.18653/v1/P19-1622
%P 6185-6190

Markdown (Informal)

[MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension](https://aclanthology.org/P19-1622) (Zhang, ACL 2019)

• MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension (Zhang, ACL 2019)

ACL

• Xuanyu Zhang. 2019. MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6185–6190, Florence, Italy. Association for Computational Linguistics.