<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="maslak-mitkov-2021-paragraph-similarity">
    <titleInfo>
        <title>Paragraph Similarity Matches for Generating Multiple-choice Test Items</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Halyna</namePart>
        <namePart type="family">Maslak</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ruslan</namePart>
        <namePart type="family">Mitkov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Student Research Workshop Associated with RANLP 2021</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Souhila</namePart>
            <namePart type="family">Djabri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dinara</namePart>
            <namePart type="family">Gimadi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tsvetomila</namePart>
            <namePart type="family">Mihaylova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova-Koleva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Multiple-choice questions (MCQs) are widely used in knowledge assessment in educational institutions, during work interviews, in entertainment quizzes and games. Although the research on the automatic or semi-automatic generation of multiple-choice test items has been conducted since the beginning of this millennium, most approaches focus on generating questions from a single sentence. In this research, a state-of-the-art method of creating questions based on multiple sentences is introduced. It was inspired by semantic similarity matches used in the translation memory component of translation management systems. The performance of two deep learning algorithms, doc2vec and SBERT, is compared for the paragraph similarity task. The experiments are performed on the ad-hoc corpus within the EU domain. For the automatic evaluation, a smaller corpus of manually selected matching paragraphs has been compiled. The results prove the good performance of Sentence Embeddings for the given task.</abstract>
    <identifier type="citekey">maslak-mitkov-2021-paragraph-similarity</identifier>
    <location>
        <url>https://aclanthology.org/2021.ranlp-srw.15</url>
    </location>
    <part>
        <date>2021-09</date>
        <extent unit="page">
            <start>99</start>
            <end>108</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Paragraph Similarity Matches for Generating Multiple-choice Test Items
%A Maslak, Halyna
%A Mitkov, Ruslan
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F maslak-mitkov-2021-paragraph-similarity
%X Multiple-choice questions (MCQs) are widely used in knowledge assessment in educational institutions, during work interviews, in entertainment quizzes and games. Although the research on the automatic or semi-automatic generation of multiple-choice test items has been conducted since the beginning of this millennium, most approaches focus on generating questions from a single sentence. In this research, a state-of-the-art method of creating questions based on multiple sentences is introduced. It was inspired by semantic similarity matches used in the translation memory component of translation management systems. The performance of two deep learning algorithms, doc2vec and SBERT, is compared for the paragraph similarity task. The experiments are performed on the ad-hoc corpus within the EU domain. For the automatic evaluation, a smaller corpus of manually selected matching paragraphs has been compiled. The results prove the good performance of Sentence Embeddings for the given task.
%U https://aclanthology.org/2021.ranlp-srw.15
%P 99-108

Markdown (Informal)

[Paragraph Similarity Matches for Generating Multiple-choice Test Items](https://aclanthology.org/2021.ranlp-srw.15) (Maslak & Mitkov, RANLP 2021)

• Paragraph Similarity Matches for Generating Multiple-choice Test Items (Maslak & Mitkov, RANLP 2021)

ACL

• Halyna Maslak and Ruslan Mitkov. 2021. Paragraph Similarity Matches for Generating Multiple-choice Test Items. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 99–108, Online. INCOMA Ltd..