<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kinugawa-etal-2024-findings">
    <titleInfo>
        <title>Findings of the WMT 2024 Shared Task on Non-Repetitive Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kazutaka</namePart>
        <namePart type="family">Kinugawa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hideya</namePart>
        <namePart type="family">Mino</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Isao</namePart>
        <namePart type="family">Goto</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Naoto</namePart>
        <namePart type="family">Shirai</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>Proceedings of the Ninth Conference on Machine Translation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Barry</namePart>
            <namePart type="family">Haddow</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tom</namePart>
            <namePart type="family">Kocmi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Philipp</namePart>
            <namePart type="family">Koehn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christof</namePart>
            <namePart type="family">Monz</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>The repetition of words in an English sentence can create a monotonous or awkward impression. In such cases, repetition should be avoided appropriately. To evaluate the performance of machine translation (MT) systems in avoiding such repetition and outputting more polished translations, we presented the shared task of controlling the lexical choice of MT systems. From Japanese–English parallel news articles, we collected several hundred sentence pairs in which the source sentences containing repeated words were translated in a style that avoided repetition. Participants were required to encourage the MT system to output tokens in a non-repetitive manner while maintaining translation quality. We conducted human and automatic evaluations of systems submitted by two teams based on an encoder-decoder Transformer and a large language model, respectively. From the experimental results and analysis, we report a series of findings on this task.</abstract>
    <identifier type="citekey">kinugawa-etal-2024-findings</identifier>
    <location>
        <url>https://aclanthology.org/2024.wmt-1.60</url>
    </location>
    <part>
        <date>2024-11</date>
        <extent unit="page">
            <start>715</start>
            <end>727</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Findings of the WMT 2024 Shared Task on Non-Repetitive Translation
%A Kinugawa, Kazutaka
%A Mino, Hideya
%A Goto, Isao
%A Shirai, Naoto
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kinugawa-etal-2024-findings
%X The repetition of words in an English sentence can create a monotonous or awkward impression. In such cases, repetition should be avoided appropriately. To evaluate the performance of machine translation (MT) systems in avoiding such repetition and outputting more polished translations, we presented the shared task of controlling the lexical choice of MT systems. From Japanese–English parallel news articles, we collected several hundred sentence pairs in which the source sentences containing repeated words were translated in a style that avoided repetition. Participants were required to encourage the MT system to output tokens in a non-repetitive manner while maintaining translation quality. We conducted human and automatic evaluations of systems submitted by two teams based on an encoder-decoder Transformer and a large language model, respectively. From the experimental results and analysis, we report a series of findings on this task.
%U https://aclanthology.org/2024.wmt-1.60
%P 715-727

Markdown (Informal)

[Findings of the WMT 2024 Shared Task on Non-Repetitive Translation](https://aclanthology.org/2024.wmt-1.60) (Kinugawa et al., WMT 2024)

• Findings of the WMT 2024 Shared Task on Non-Repetitive Translation (Kinugawa et al., WMT 2024)

ACL

• Kazutaka Kinugawa, Hideya Mino, Isao Goto, and Naoto Shirai. 2024. Findings of the WMT 2024 Shared Task on Non-Repetitive Translation. In Proceedings of the Ninth Conference on Machine Translation, pages 715–727, Miami, Florida, USA. Association for Computational Linguistics.