<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="satria-tokunaga-2017-evaluation">
    <titleInfo>
        <title>Evaluation of Automatically Generated Pronoun Reference Questions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Arief</namePart>
        <namePart type="given">Yudha</namePart>
        <namePart type="family">Satria</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Takenobu</namePart>
        <namePart type="family">Tokunaga</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jill</namePart>
            <namePart type="family">Burstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Leacock</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Copenhagen, Denmark</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This study provides a detailed analysis of evaluation of English pronoun reference questions which are created automatically by machine. Pronoun reference questions are multiple choice questions that ask test takers to choose an antecedent of a target pronoun in a reading passage from four options. The evaluation was performed from two perspectives: the perspective of English teachers and that of English learners. Item analysis suggests that machine-generated questions achieve comparable quality with human-made questions. Correlation analysis revealed a strong correlation between the scores of machine-generated questions and that of human-made questions.</abstract>
    <identifier type="citekey">satria-tokunaga-2017-evaluation</identifier>
    <identifier type="doi">10.18653/v1/W17-5008</identifier>
    <location>
        <url>https://aclanthology.org/W17-5008</url>
    </location>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>76</start>
            <end>85</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Evaluation of Automatically Generated Pronoun Reference Questions
%A Satria, Arief Yudha
%A Tokunaga, Takenobu
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F satria-tokunaga-2017-evaluation
%X This study provides a detailed analysis of evaluation of English pronoun reference questions which are created automatically by machine. Pronoun reference questions are multiple choice questions that ask test takers to choose an antecedent of a target pronoun in a reading passage from four options. The evaluation was performed from two perspectives: the perspective of English teachers and that of English learners. Item analysis suggests that machine-generated questions achieve comparable quality with human-made questions. Correlation analysis revealed a strong correlation between the scores of machine-generated questions and that of human-made questions.
%R 10.18653/v1/W17-5008
%U https://aclanthology.org/W17-5008
%U https://doi.org/10.18653/v1/W17-5008
%P 76-85

Markdown (Informal)

[Evaluation of Automatically Generated Pronoun Reference Questions](https://aclanthology.org/W17-5008) (Satria & Tokunaga, BEA 2017)

• Evaluation of Automatically Generated Pronoun Reference Questions (Satria & Tokunaga, BEA 2017)

ACL

• Arief Yudha Satria and Takenobu Tokunaga. 2017. Evaluation of Automatically Generated Pronoun Reference Questions. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 76–85, Copenhagen, Denmark. Association for Computational Linguistics.