<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cavusoglu-etal-2024-disgem">
    <titleInfo>
        <title>DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Devrim</namePart>
        <namePart type="family">Çavuşoğlu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Seçil</namePart>
        <namePart type="family">Şen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ulaş</namePart>
        <namePart type="family">Sert</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Miami, Florida, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question generation, the creation of distractors for multiple-choice questions (MCQ) remains a challenging task. In this work, we present a simple, generic framework for distractor generation using readily available Pre-trained Language Models (PLMs). Unlike previous methods, our framework relies solely on pre-trained language models and does not require additional training on specific datasets. Building upon previous research, we introduce a two-stage framework consisting of candidate generation and candidate selection. Our proposed distractor generation framework outperforms previous methods without the need for training or fine-tuning. Human evaluations confirm that our approach produces more effective and engaging distractors. The related codebase is publicly available at https://github.com/obss/disgem.</abstract>
    <identifier type="citekey">cavusoglu-etal-2024-disgem</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-emnlp.568</url>
    </location>
    <part>
        <date>2024-11</date>
        <extent unit="page">
            <start>9714</start>
            <end>9732</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
%A Çavuşoğlu, Devrim
%A Şen, Seçil
%A Sert, Ulaş
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F cavusoglu-etal-2024-disgem
%X Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question generation, the creation of distractors for multiple-choice questions (MCQ) remains a challenging task. In this work, we present a simple, generic framework for distractor generation using readily available Pre-trained Language Models (PLMs). Unlike previous methods, our framework relies solely on pre-trained language models and does not require additional training on specific datasets. Building upon previous research, we introduce a two-stage framework consisting of candidate generation and candidate selection. Our proposed distractor generation framework outperforms previous methods without the need for training or fine-tuning. Human evaluations confirm that our approach produces more effective and engaging distractors. The related codebase is publicly available at https://github.com/obss/disgem.
%U https://aclanthology.org/2024.findings-emnlp.568
%P 9714-9732

Markdown (Informal)

[DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking](https://aclanthology.org/2024.findings-emnlp.568) (Çavuşoğlu et al., Findings 2024)

• DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking (Çavuşoğlu et al., Findings 2024)

ACL

• Devrim Çavuşoğlu, Seçil Şen, and Ulaş Sert. 2024. DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9714–9732, Miami, Florida, USA. Association for Computational Linguistics.