<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="senderowicz-2024-comment">
    <titleInfo>
        <title>From “Comment allez-vous?” to “Comment ça va?”: Leveraging Large Language Models to Automate Formality Adaptation in Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Vera</namePart>
        <namePart type="family">Senderowicz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Martindale</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Janice</namePart>
            <namePart type="family">Campbell</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Konstantin</namePart>
            <namePart type="family">Savenkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shivali</namePart>
            <namePart type="family">Goel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Machine Translation in the Americas</publisher>
            <place>
                <placeTerm type="text">Chicago, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The evolution of machine translation (MT) has seen significant advancements in data cleaning and post-editing methodologies, but numerous cases requiring semantic comprehension have still necessitated human intervention—until the emergence of Large Language Models (LLMs). In our research, we have explored an innovative application of Generative AI (Gen AI) to adapt bilingual content’s target segments from a formal to an informal register, in scenarios where the source language lacks explicit grammatical markers for formality and thus is grammatically bivalent in that sense. In this session, we will demonstrate how LLMs, enhanced by supplementary methodologies such as fine-tuning and combined with other, more legacy language models, can efficiently perform this formality adaptation task. We aim to showcase best practices for leveraging Gen AI in adapting bilingual content registers, highlighting the potential for cost reduction and quality enhancement in translation processes.</abstract>
    <identifier type="citekey">senderowicz-2024-comment</identifier>
    <location>
        <url>https://aclanthology.org/2024.amta-presentations.16</url>
    </location>
    <part>
        <date>2024-09</date>
        <extent unit="page">
            <start>237</start>
            <end>254</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T From “Comment allez-vous?” to “Comment ça va?”: Leveraging Large Language Models to Automate Formality Adaptation in Translation
%A Senderowicz, Vera
%Y Martindale, Marianna
%Y Campbell, Janice
%Y Savenkov, Konstantin
%Y Goel, Shivali
%S Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations)
%D 2024
%8 September
%I Association for Machine Translation in the Americas
%C Chicago, USA
%F senderowicz-2024-comment
%X The evolution of machine translation (MT) has seen significant advancements in data cleaning and post-editing methodologies, but numerous cases requiring semantic comprehension have still necessitated human intervention—until the emergence of Large Language Models (LLMs). In our research, we have explored an innovative application of Generative AI (Gen AI) to adapt bilingual content’s target segments from a formal to an informal register, in scenarios where the source language lacks explicit grammatical markers for formality and thus is grammatically bivalent in that sense. In this session, we will demonstrate how LLMs, enhanced by supplementary methodologies such as fine-tuning and combined with other, more legacy language models, can efficiently perform this formality adaptation task. We aim to showcase best practices for leveraging Gen AI in adapting bilingual content registers, highlighting the potential for cost reduction and quality enhancement in translation processes.
%U https://aclanthology.org/2024.amta-presentations.16
%P 237-254

Markdown (Informal)

[From “Comment allez-vous?” to “Comment ça va?”: Leveraging Large Language Models to Automate Formality Adaptation in Translation](https://aclanthology.org/2024.amta-presentations.16) (Senderowicz, AMTA 2024)

• From “Comment allez-vous?” to “Comment ça va?”: Leveraging Large Language Models to Automate Formality Adaptation in Translation (Senderowicz, AMTA 2024)

ACL

• Vera Senderowicz. 2024. From “Comment allez-vous?” to “Comment ça va?”: Leveraging Large Language Models to Automate Formality Adaptation in Translation. In Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations), pages 237–254, Chicago, USA. Association for Machine Translation in the Americas.