<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ang-etal-2023-socratic">
    <titleInfo>
        <title>Socratic Question Generation: A Novel Dataset, Models, and Evaluation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Beng</namePart>
        <namePart type="given">Heng</namePart>
        <namePart type="family">Ang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sujatha</namePart>
        <namePart type="given">Das</namePart>
        <namePart type="family">Gollapalli</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">See-Kiong</namePart>
        <namePart type="family">Ng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dubrovnik, Croatia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Socratic questioning is a form of reflective inquiry often employed in education to encourage critical thinking in students, and to elicit awareness of beliefs and perspectives in a subject during therapeutic counseling. Specific types of Socratic questions are employed for enabling reasoning and alternate views against the context of individual personal opinions on a topic. Socratic contexts are different from traditional question generation contexts where “answer-seeking” questions are generated against a given formal passage on a topic, narrative stories or conversations. We present SocratiQ, the first large dataset of 110K (question, context) pairs for enabling studies on Socratic Question Generation (SoQG). We provide an in-depth study on the various types of Socratic questions and present models for generating Socratic questions against a given context through prompt tuning. Our automated and human evaluation results demonstrate that our SoQG models can produce realistic, type-sensitive, human-like Socratic questions enabling potential applications in counseling and coaching.</abstract>
    <identifier type="citekey">ang-etal-2023-socratic</identifier>
    <identifier type="doi">10.18653/v1/2023.eacl-main.12</identifier>
    <location>
        <url>https://aclanthology.org/2023.eacl-main.12</url>
    </location>
    <part>
        <date>2023-05</date>
        <extent unit="page">
            <start>147</start>
            <end>165</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Socratic Question Generation: A Novel Dataset, Models, and Evaluation
%A Ang, Beng Heng
%A Gollapalli, Sujatha Das
%A Ng, See-Kiong
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F ang-etal-2023-socratic
%X Socratic questioning is a form of reflective inquiry often employed in education to encourage critical thinking in students, and to elicit awareness of beliefs and perspectives in a subject during therapeutic counseling. Specific types of Socratic questions are employed for enabling reasoning and alternate views against the context of individual personal opinions on a topic. Socratic contexts are different from traditional question generation contexts where “answer-seeking” questions are generated against a given formal passage on a topic, narrative stories or conversations. We present SocratiQ, the first large dataset of 110K (question, context) pairs for enabling studies on Socratic Question Generation (SoQG). We provide an in-depth study on the various types of Socratic questions and present models for generating Socratic questions against a given context through prompt tuning. Our automated and human evaluation results demonstrate that our SoQG models can produce realistic, type-sensitive, human-like Socratic questions enabling potential applications in counseling and coaching.
%R 10.18653/v1/2023.eacl-main.12
%U https://aclanthology.org/2023.eacl-main.12
%U https://doi.org/10.18653/v1/2023.eacl-main.12
%P 147-165

Markdown (Informal)

[Socratic Question Generation: A Novel Dataset, Models, and Evaluation](https://aclanthology.org/2023.eacl-main.12) (Ang et al., EACL 2023)

• Socratic Question Generation: A Novel Dataset, Models, and Evaluation (Ang et al., EACL 2023)

ACL

• Beng Heng Ang, Sujatha Das Gollapalli, and See-Kiong Ng. 2023. Socratic Question Generation: A Novel Dataset, Models, and Evaluation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 147–165, Dubrovnik, Croatia. Association for Computational Linguistics.