<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ostermann-etal-2019-commonsense">
    <titleInfo>
        <title>Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Simon</namePart>
        <namePart type="family">Ostermann</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sheng</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="family">Roth</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Peter</namePart>
        <namePart type="family">Clark</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Ostermann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sheng</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Roth</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Peter</namePart>
            <namePart type="family">Clark</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hong Kong, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper reports on the results of the shared tasks of the COIN workshop at EMNLP-IJCNLP 2019. The tasks consisted of two machine comprehension evaluations, each of which tested a system’s ability to answer questions/queries about a text. Both evaluations were designed such that systems need to exploit commonsense knowledge, for example, in the form of inferences over information that is available in the common ground but not necessarily mentioned in the text. A total of five participating teams submitted systems for the shared tasks, with the best submitted system achieving 90.6% accuracy and 83.7% F1-score on task 1 and task 2, respectively.</abstract>
    <identifier type="citekey">ostermann-etal-2019-commonsense</identifier>
    <identifier type="doi">10.18653/v1/D19-6007</identifier>
    <location>
        <url>https://aclanthology.org/D19-6007</url>
    </location>
    <part>
        <date>2019-11</date>
        <extent unit="page">
            <start>66</start>
            <end>74</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report
%A Ostermann, Simon
%A Zhang, Sheng
%A Roth, Michael
%A Clark, Peter
%Y Ostermann, Simon
%Y Zhang, Sheng
%Y Roth, Michael
%Y Clark, Peter
%S Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ostermann-etal-2019-commonsense
%X This paper reports on the results of the shared tasks of the COIN workshop at EMNLP-IJCNLP 2019. The tasks consisted of two machine comprehension evaluations, each of which tested a system’s ability to answer questions/queries about a text. Both evaluations were designed such that systems need to exploit commonsense knowledge, for example, in the form of inferences over information that is available in the common ground but not necessarily mentioned in the text. A total of five participating teams submitted systems for the shared tasks, with the best submitted system achieving 90.6% accuracy and 83.7% F1-score on task 1 and task 2, respectively.
%R 10.18653/v1/D19-6007
%U https://aclanthology.org/D19-6007
%U https://doi.org/10.18653/v1/D19-6007
%P 66-74

Markdown (Informal)

[Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report](https://aclanthology.org/D19-6007) (Ostermann et al., 2019)

• Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report (Ostermann et al., 2019)

ACL

• Simon Ostermann, Sheng Zhang, Michael Roth, and Peter Clark. 2019. Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report. In Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing, pages 66–74, Hong Kong, China. Association for Computational Linguistics.