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        <title>A Neural Question Answering Model Based on Semi-Structured Tables</title>
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        <namePart type="given">Hao</namePart>
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        <namePart type="given">Xiaodong</namePart>
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    <name type="personal">
        <namePart type="given">Shuming</namePart>
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    <name type="personal">
        <namePart type="given">Xu</namePart>
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    <name type="personal">
        <namePart type="given">Houfeng</namePart>
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    <name type="personal">
        <namePart type="given">Mengxiang</namePart>
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    <abstract>Most question answering (QA) systems are based on raw text and structured knowledge graph. However, raw text corpora are hard for QA system to understand, and structured knowledge graph needs intensive manual work, while it is relatively easy to obtain semi-structured tables from many sources directly, or build them automatically. In this paper, we build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. Our system answers queries by two steps. First, it finds the most similar tables. Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer. The system is evaluated with TabMCQ dataset, and gets a huge improvement compared to the state of the art.</abstract>
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    <part>
        <date>2018-08</date>
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            <start>1941</start>
            <end>1951</end>
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%0 Conference Proceedings
%T A Neural Question Answering Model Based on Semi-Structured Tables
%A Wang, Hao
%A Zhang, Xiaodong
%A Ma, Shuming
%A Sun, Xu
%A Wang, Houfeng
%A Wang, Mengxiang
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F wang-etal-2018-neural-question
%X Most question answering (QA) systems are based on raw text and structured knowledge graph. However, raw text corpora are hard for QA system to understand, and structured knowledge graph needs intensive manual work, while it is relatively easy to obtain semi-structured tables from many sources directly, or build them automatically. In this paper, we build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. Our system answers queries by two steps. First, it finds the most similar tables. Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer. The system is evaluated with TabMCQ dataset, and gets a huge improvement compared to the state of the art.
%U https://aclanthology.org/C18-1165
%P 1941-1951

Markdown (Informal)

[A Neural Question Answering Model Based on Semi-Structured Tables](https://aclanthology.org/C18-1165) (Wang et al., COLING 2018)

• A Neural Question Answering Model Based on Semi-Structured Tables (Wang et al., COLING 2018)

ACL

• Hao Wang, Xiaodong Zhang, Shuming Ma, Xu Sun, Houfeng Wang, and Mengxiang Wang. 2018. A Neural Question Answering Model Based on Semi-Structured Tables. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1941–1951, Santa Fe, New Mexico, USA. Association for Computational Linguistics.