<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kao-chang-2022-applying">
    <titleInfo>
        <title>Applying Information Extraction to Storybook Question and Answer Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kai-Yen</namePart>
        <namePart type="family">Kao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chia-Hui</namePart>
        <namePart type="family">Chang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <language>
        <languageTerm type="text">Chinese</languageTerm>
        <languageTerm type="code" authority="iso639-2b">chi</languageTerm>
    </language>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)</title>
        </titleInfo>
        <originInfo>
            <publisher>The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)</publisher>
            <place>
                <placeTerm type="text">Taipei, Taiwan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>For educators, how to generate high quality question-answer pairs from story text is a time-consuming and labor-intensive task. The purpose is not to make students unable to answer, but to ensure that students understand the story text through the generated question-answer pairs. In this paper, we improve the FairyTaleQA question generation method by incorporating question type and its definition to the input for fine-tuning the BART (Lewis et al., 2020) model. Furthermore, we make use of the entity and relation extraction from (Zhong and Chen, 2021) as an element of template-based question generation.</abstract>
    <identifier type="citekey">kao-chang-2022-applying</identifier>
    <location>
        <url>https://aclanthology.org/2022.rocling-1.36</url>
    </location>
    <part>
        <date>2022-11</date>
        <extent unit="page">
            <start>289</start>
            <end>298</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Applying Information Extraction to Storybook Question and Answer Generation
%A Kao, Kai-Yen
%A Chang, Chia-Hui
%S Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
%D 2022
%8 November
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taipei, Taiwan
%G Chinese
%F kao-chang-2022-applying
%X For educators, how to generate high quality question-answer pairs from story text is a time-consuming and labor-intensive task. The purpose is not to make students unable to answer, but to ensure that students understand the story text through the generated question-answer pairs. In this paper, we improve the FairyTaleQA question generation method by incorporating question type and its definition to the input for fine-tuning the BART (Lewis et al., 2020) model. Furthermore, we make use of the entity and relation extraction from (Zhong and Chen, 2021) as an element of template-based question generation.
%U https://aclanthology.org/2022.rocling-1.36
%P 289-298

Markdown (Informal)

[Applying Information Extraction to Storybook Question and Answer Generation](https://aclanthology.org/2022.rocling-1.36) (Kao & Chang, ROCLING 2022)

• Applying Information Extraction to Storybook Question and Answer Generation (Kao & Chang, ROCLING 2022)

ACL

• Kai-Yen Kao and Chia-Hui Chang. 2022. Applying Information Extraction to Storybook Question and Answer Generation. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), pages 289–298, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).