<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nishida-etal-2024-generating">
    <titleInfo>
        <title>Generating Diverse Translation with Perturbed kNN-MT</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuto</namePart>
        <namePart type="family">Nishida</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Makoto</namePart>
        <namePart type="family">Morishita</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hidetaka</namePart>
        <namePart type="family">Kamigaito</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Taro</namePart>
        <namePart type="family">Watanabe</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 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Neele</namePart>
            <namePart type="family">Falk</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sara</namePart>
            <namePart type="family">Papi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mike</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">St. Julian’s, Malta</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Generating multiple translation candidates would enable users to choose the one that satisfies their needs.Although there has been work on diversified generation, there exists room for improving the diversity mainly because the previous methods do not address the overcorrection problem—the model underestimates a prediction that is largely different from the training data, even if that prediction is likely.This paper proposes methods that generate more diverse translations by introducing perturbed k-nearest neighbor machine translation (kNN-MT).Our methods expand the search space of kNN-MT and help incorporate diverse words into candidates by addressing the overcorrection problem.Our experiments show that the proposed methods drastically improve candidate diversity and control the degree of diversity by tuning the perturbation’s magnitude.</abstract>
    <identifier type="citekey">nishida-etal-2024-generating</identifier>
    <location>
        <url>https://aclanthology.org/2024.eacl-srw.2</url>
    </location>
    <part>
        <date>2024-03</date>
        <extent unit="page">
            <start>9</start>
            <end>31</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Generating Diverse Translation with Perturbed kNN-MT
%A Nishida, Yuto
%A Morishita, Makoto
%A Kamigaito, Hidetaka
%A Watanabe, Taro
%Y Falk, Neele
%Y Papi, Sara
%Y Zhang, Mike
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F nishida-etal-2024-generating
%X Generating multiple translation candidates would enable users to choose the one that satisfies their needs.Although there has been work on diversified generation, there exists room for improving the diversity mainly because the previous methods do not address the overcorrection problem—the model underestimates a prediction that is largely different from the training data, even if that prediction is likely.This paper proposes methods that generate more diverse translations by introducing perturbed k-nearest neighbor machine translation (kNN-MT).Our methods expand the search space of kNN-MT and help incorporate diverse words into candidates by addressing the overcorrection problem.Our experiments show that the proposed methods drastically improve candidate diversity and control the degree of diversity by tuning the perturbation’s magnitude.
%U https://aclanthology.org/2024.eacl-srw.2
%P 9-31

Markdown (Informal)

[Generating Diverse Translation with Perturbed kNN-MT](https://aclanthology.org/2024.eacl-srw.2) (Nishida et al., EACL 2024)

• Generating Diverse Translation with Perturbed kNN-MT (Nishida et al., EACL 2024)

ACL

• Yuto Nishida, Makoto Morishita, Hidetaka Kamigaito, and Taro Watanabe. 2024. Generating Diverse Translation with Perturbed kNN-MT. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 9–31, St. Julian’s, Malta. Association for Computational Linguistics.