<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nayak-etal-2023-instance">
    <titleInfo>
        <title>Instance-Based Domain Adaptation for Improving Terminology Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Prashanth</namePart>
        <namePart type="family">Nayak</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">John</namePart>
        <namePart type="family">Kelleher</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rejwanul</namePart>
        <namePart type="family">Haque</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Andy</namePart>
        <namePart type="family">Way</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Masao</namePart>
            <namePart type="family">Utiyama</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rui</namePart>
            <namePart type="family">Wang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Asia-Pacific Association for Machine Translation</publisher>
            <place>
                <placeTerm type="text">Macau SAR, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Terms are essential indicators of a domain, and domain term translation is dealt with priority in any translation workflow. Translation service providers who use machine translation (MT) expect term translation to be unambiguous and consistent with the context and domain in question. Although current state-of-the-art neural MT (NMT) models are able to produce high-quality translations for many languages, they are still not at the level required when it comes to translating domain-specific terms. This study presents a terminology-aware instance- based adaptation method for improving terminology translation in NMT. We conducted our experiments for French-to-English and found that our proposed approach achieves a statistically significant improvement over the baseline NMT system in translating domain-specific terms. Specifically, the translation of multi-word terms is improved by 6.7% compared to the strong baseline.</abstract>
    <identifier type="citekey">nayak-etal-2023-instance</identifier>
    <location>
        <url>https://aclanthology.org/2023.mtsummit-research.19/</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>222</start>
            <end>234</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Instance-Based Domain Adaptation for Improving Terminology Translation
%A Nayak, Prashanth
%A Kelleher, John
%A Haque, Rejwanul
%A Way, Andy
%Y Utiyama, Masao
%Y Wang, Rui
%S Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F nayak-etal-2023-instance
%X Terms are essential indicators of a domain, and domain term translation is dealt with priority in any translation workflow. Translation service providers who use machine translation (MT) expect term translation to be unambiguous and consistent with the context and domain in question. Although current state-of-the-art neural MT (NMT) models are able to produce high-quality translations for many languages, they are still not at the level required when it comes to translating domain-specific terms. This study presents a terminology-aware instance- based adaptation method for improving terminology translation in NMT. We conducted our experiments for French-to-English and found that our proposed approach achieves a statistically significant improvement over the baseline NMT system in translating domain-specific terms. Specifically, the translation of multi-word terms is improved by 6.7% compared to the strong baseline.
%U https://aclanthology.org/2023.mtsummit-research.19/
%P 222-234

Markdown (Informal)

[Instance-Based Domain Adaptation for Improving Terminology Translation](https://aclanthology.org/2023.mtsummit-research.19/) (Nayak et al., MTSummit 2023)

• Instance-Based Domain Adaptation for Improving Terminology Translation (Nayak et al., MTSummit 2023)

ACL

• Prashanth Nayak, John Kelleher, Rejwanul Haque, and Andy Way. 2023. Instance-Based Domain Adaptation for Improving Terminology Translation. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 222–234, Macau SAR, China. Asia-Pacific Association for Machine Translation.