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        <title>A Preliminary Study of AI Agent Model in Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Ahrii</namePart>
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            <title>Proceedings of the Tenth Conference on Machine Translation</title>
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        <name type="personal">
            <namePart type="given">Barry</namePart>
            <namePart type="family">Haddow</namePart>
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        <name type="personal">
            <namePart type="given">Tom</namePart>
            <namePart type="family">Kocmi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Philipp</namePart>
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        <name type="personal">
            <namePart type="given">Christof</namePart>
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    <abstract>We present IR_Multi-agentMT, our submission to the WMT25 General Shared Task. The system adopts an AI-agent paradigm implemented through a multi-agent workflow, Prompt Chaining, in combination with RUBRIC-MQM, an automatic MQM-based error annotation metric. Our primary configuration follows the Translate–Postedit–Proofread paradigm, where each stage progressively enhances translation quality. We conduct a preliminary study to investigate (i) the impact of initial translation quality and (ii) the effect of enforcing explicit responses from the Postedit Agent. Our findings highlight the importance of both factors in shaping the overall performance of multi-agent translation systems.</abstract>
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        <url>https://aclanthology.org/2025.wmt-1.32/</url>
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        <date>2025-11</date>
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%0 Conference Proceedings
%T A Preliminary Study of AI Agent Model in Machine Translation
%A Kim, Ahrii
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F kim-2025-preliminary
%X We present IR_Multi-agentMT, our submission to the WMT25 General Shared Task. The system adopts an AI-agent paradigm implemented through a multi-agent workflow, Prompt Chaining, in combination with RUBRIC-MQM, an automatic MQM-based error annotation metric. Our primary configuration follows the Translate–Postedit–Proofread paradigm, where each stage progressively enhances translation quality. We conduct a preliminary study to investigate (i) the impact of initial translation quality and (ii) the effect of enforcing explicit responses from the Postedit Agent. Our findings highlight the importance of both factors in shaping the overall performance of multi-agent translation systems.
%U https://aclanthology.org/2025.wmt-1.32/
%P 583-586

Markdown (Informal)

[A Preliminary Study of AI Agent Model in Machine Translation](https://aclanthology.org/2025.wmt-1.32/) (Kim, WMT 2025)

• A Preliminary Study of AI Agent Model in Machine Translation (Kim, WMT 2025)

ACL

• Ahrii Kim. 2025. A Preliminary Study of AI Agent Model in Machine Translation. In Proceedings of the Tenth Conference on Machine Translation, pages 583–586, Suzhou, China. Association for Computational Linguistics.