<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sun-etal-2018-reading">
    <titleInfo>
        <title>Reading Comprehension with Graph-based Temporal-Casual Reasoning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yawei</namePart>
        <namePart type="family">Sun</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gong</namePart>
        <namePart type="family">Cheng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yuzhong</namePart>
        <namePart type="family">Qu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pierre</namePart>
            <namePart type="family">Isabelle</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santa Fe, New Mexico, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Complex questions in reading comprehension tasks require integrating information from multiple sentences. In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text based on dependencies, and rank answers by aligning event graphs. In particular, the alignments are constrained by graph-based reasoning to ensure temporal and causal agreement. Our focused approach self-adaptively complements existing solutions; it is automatically triggered only when applicable. Experiments on RACE and MCTest show that state-of-the-art methods are notably improved by using our approach as an add-on.</abstract>
    <identifier type="citekey">sun-etal-2018-reading</identifier>
    <location>
        <url>https://aclanthology.org/C18-1069</url>
    </location>
    <part>
        <date>2018-08</date>
        <extent unit="page">
            <start>806</start>
            <end>817</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Reading Comprehension with Graph-based Temporal-Casual Reasoning
%A Sun, Yawei
%A Cheng, Gong
%A Qu, Yuzhong
%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 sun-etal-2018-reading
%X Complex questions in reading comprehension tasks require integrating information from multiple sentences. In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text based on dependencies, and rank answers by aligning event graphs. In particular, the alignments are constrained by graph-based reasoning to ensure temporal and causal agreement. Our focused approach self-adaptively complements existing solutions; it is automatically triggered only when applicable. Experiments on RACE and MCTest show that state-of-the-art methods are notably improved by using our approach as an add-on.
%U https://aclanthology.org/C18-1069
%P 806-817

Markdown (Informal)

[Reading Comprehension with Graph-based Temporal-Casual Reasoning](https://aclanthology.org/C18-1069) (Sun et al., COLING 2018)

• Reading Comprehension with Graph-based Temporal-Casual Reasoning (Sun et al., COLING 2018)

ACL

• Yawei Sun, Gong Cheng, and Yuzhong Qu. 2018. Reading Comprehension with Graph-based Temporal-Casual Reasoning. In Proceedings of the 27th International Conference on Computational Linguistics, pages 806–817, Santa Fe, New Mexico, USA. Association for Computational Linguistics.