<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jahangir-etal-2024-complex">
    <titleInfo>
        <title>Complex question generation using discourse-based data augmentation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Khushnur</namePart>
        <namePart type="family">Jahangir</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Philippe</namePart>
        <namePart type="family">Muller</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chloé</namePart>
        <namePart type="family">Braud</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-03</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Strube</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chloe</namePart>
            <namePart type="family">Braud</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christian</namePart>
            <namePart type="family">Hardmeier</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junyi</namePart>
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sharid</namePart>
            <namePart type="family">Loaiciga</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Amir</namePart>
            <namePart type="family">Zeldes</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chuyuan</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">St. Julians, Malta</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Question Generation (QG), the process of generating meaningful questions from a given context, has proven to be useful for several tasks such as question answering or FAQ generation. While most existing QG techniques generate simple, fact-based questions, this research aims to generate questions that can have complex answers (e.g. “why” questions). We propose a data augmentation method that uses discourse relations to create such questions, and experiment on existing English data. Our approach generates questions based solely on the context without answer supervision, in order to enhance question diversity and complexity. We use an encoder-decoder trained on the augmented dataset to generate either one question or multiple questions at a time, and show that the latter improves over the baseline model when doing a human quality evaluation, without degrading performance according to standard automated metrics.</abstract>
    <identifier type="citekey">jahangir-etal-2024-complex</identifier>
    <location>
        <url>https://aclanthology.org/2024.codi-1.10</url>
    </location>
    <part>
        <date>2024-03</date>
        <extent unit="page">
            <start>105</start>
            <end>119</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Complex question generation using discourse-based data augmentation
%A Jahangir, Khushnur
%A Muller, Philippe
%A Braud, Chloé
%Y Strube, Michael
%Y Braud, Chloe
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%Y Li, Chuyuan
%S Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F jahangir-etal-2024-complex
%X Question Generation (QG), the process of generating meaningful questions from a given context, has proven to be useful for several tasks such as question answering or FAQ generation. While most existing QG techniques generate simple, fact-based questions, this research aims to generate questions that can have complex answers (e.g. “why” questions). We propose a data augmentation method that uses discourse relations to create such questions, and experiment on existing English data. Our approach generates questions based solely on the context without answer supervision, in order to enhance question diversity and complexity. We use an encoder-decoder trained on the augmented dataset to generate either one question or multiple questions at a time, and show that the latter improves over the baseline model when doing a human quality evaluation, without degrading performance according to standard automated metrics.
%U https://aclanthology.org/2024.codi-1.10
%P 105-119

Markdown (Informal)

[Complex question generation using discourse-based data augmentation](https://aclanthology.org/2024.codi-1.10) (Jahangir et al., CODI-WS 2024)

• Complex question generation using discourse-based data augmentation (Jahangir et al., CODI-WS 2024)

ACL

• Khushnur Jahangir, Philippe Muller, and Chloé Braud. 2024. Complex question generation using discourse-based data augmentation. In Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024), pages 105–119, St. Julians, Malta. Association for Computational Linguistics.