<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="donati-etal-2024-generation">
    <titleInfo>
        <title>Generation and Evaluation of English Grammar Multiple-Choice Cloze Exercises</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Nicolò</namePart>
        <namePart type="family">Donati</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Matteo</namePart>
        <namePart type="family">Periani</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Paolo</namePart>
        <namePart type="family">Di Natale</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Giuseppe</namePart>
        <namePart type="family">Savino</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Paolo</namePart>
        <namePart type="family">Torroni</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Felice</namePart>
            <namePart type="family">Dell’Orletta</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alessandro</namePart>
            <namePart type="family">Lenci</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simonetta</namePart>
            <namePart type="family">Montemagni</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rachele</namePart>
            <namePart type="family">Sprugnoli</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>CEUR Workshop Proceedings</publisher>
            <place>
                <placeTerm type="text">Pisa, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-12-210-7060-6</identifier>
    </relatedItem>
    <abstract>English grammar Multiple-Choice Cloze (MCC) exercises are crucial for improving learners’ grammatical proficiency andcomprehension skills. However, creating these exercises is labour-intensive and requires expert knowledge. Effective MCCexercises must be contextually relevant and engaging, incorporating distractors—plausible but incorrect alternatives—tobalance difficulty and maintain learner motivation. Despite the increasing interest in utilizing large language models (LLMs)in education, their application in generating English grammar MCC exercises is still limited. Previous methods typicallyimpose constraints on LLMs, producing grammatically correct yet uncreative results. This paper explores the potentialof LLMs to independently generate diverse and contextually relevant MCC exercises without predefined limitations. Wehypothesize that LLMs can craft self-contained sentences that foster learner‘s communicative competence. Our analysisof existing MCC exercise datasets revealed issues of diversity, completeness, and correctness. Furthermore, we addressthe lack of a standardized automatic metric for evaluating the quality of generated exercises. Our contributions includedeveloping an LLM-based solution for generating MCC exercises, curating a comprehensive dataset spanning 19 grammartopics, and proposing an automatic metric validated against human expert evaluations. This work aims to advance theautomatic generation of English grammar MCC exercises, enhancing both their quality and creativity.</abstract>
    <identifier type="citekey">donati-etal-2024-generation</identifier>
    <location>
        <url>https://aclanthology.org/2024.clicit-1.39/</url>
    </location>
    <part>
        <date>2024-12</date>
        <extent unit="page">
            <start>325</start>
            <end>334</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Generation and Evaluation of English Grammar Multiple-Choice Cloze Exercises
%A Donati, Nicolò
%A Periani, Matteo
%A Di Natale, Paolo
%A Savino, Giuseppe
%A Torroni, Paolo
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F donati-etal-2024-generation
%X English grammar Multiple-Choice Cloze (MCC) exercises are crucial for improving learners’ grammatical proficiency andcomprehension skills. However, creating these exercises is labour-intensive and requires expert knowledge. Effective MCCexercises must be contextually relevant and engaging, incorporating distractors—plausible but incorrect alternatives—tobalance difficulty and maintain learner motivation. Despite the increasing interest in utilizing large language models (LLMs)in education, their application in generating English grammar MCC exercises is still limited. Previous methods typicallyimpose constraints on LLMs, producing grammatically correct yet uncreative results. This paper explores the potentialof LLMs to independently generate diverse and contextually relevant MCC exercises without predefined limitations. Wehypothesize that LLMs can craft self-contained sentences that foster learner‘s communicative competence. Our analysisof existing MCC exercise datasets revealed issues of diversity, completeness, and correctness. Furthermore, we addressthe lack of a standardized automatic metric for evaluating the quality of generated exercises. Our contributions includedeveloping an LLM-based solution for generating MCC exercises, curating a comprehensive dataset spanning 19 grammartopics, and proposing an automatic metric validated against human expert evaluations. This work aims to advance theautomatic generation of English grammar MCC exercises, enhancing both their quality and creativity.
%U https://aclanthology.org/2024.clicit-1.39/
%P 325-334

Markdown (Informal)

[Generation and Evaluation of English Grammar Multiple-Choice Cloze Exercises](https://aclanthology.org/2024.clicit-1.39/) (Donati et al., CLiC-it 2024)

• Generation and Evaluation of English Grammar Multiple-Choice Cloze Exercises (Donati et al., CLiC-it 2024)

ACL

• Nicolò Donati, Matteo Periani, Paolo Di Natale, Giuseppe Savino, and Paolo Torroni. 2024. Generation and Evaluation of English Grammar Multiple-Choice Cloze Exercises. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 325–334, Pisa, Italy. CEUR Workshop Proceedings.