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        <title>MQM-Chat: Multidimensional Quality Metrics for Chat Translation</title>
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    <abstract>The complexities of chats, such as the stylized contents specific to source segments and dialogue consistency, pose significant challenges for machine translation. Recognizing the need for a precise evaluation metric to address the issues associated with chat translation, this study introduces Multidimensional Quality Metrics for Chat Translation (MQM-Chat), which encompasses seven error types, including three specifically designed for chat translations: ambiguity and disambiguation, buzzword or loanword issues, and dialogue inconsistency. In this study, human annotations were applied to the translations of chat data generated by five translation models. Based on the error distribution of MQM-Chat and the performance of relabeling errors into chat-specific types, we concluded that MQM-Chat effectively classified the errors while highlighting chat-specific issues explicitly. The results demonstrate that MQM-Chat can qualify both the lexical accuracy and semantical accuracy of translation models in chat translation tasks.</abstract>
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%0 Conference Proceedings
%T MQM-Chat: Multidimensional Quality Metrics for Chat Translation
%A Li, Yunmeng
%A Suzuki, Jun
%A Morishita, Makoto
%A Abe, Kaori
%A Inui, Kentaro
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F li-etal-2025-mqm
%X The complexities of chats, such as the stylized contents specific to source segments and dialogue consistency, pose significant challenges for machine translation. Recognizing the need for a precise evaluation metric to address the issues associated with chat translation, this study introduces Multidimensional Quality Metrics for Chat Translation (MQM-Chat), which encompasses seven error types, including three specifically designed for chat translations: ambiguity and disambiguation, buzzword or loanword issues, and dialogue inconsistency. In this study, human annotations were applied to the translations of chat data generated by five translation models. Based on the error distribution of MQM-Chat and the performance of relabeling errors into chat-specific types, we concluded that MQM-Chat effectively classified the errors while highlighting chat-specific issues explicitly. The results demonstrate that MQM-Chat can qualify both the lexical accuracy and semantical accuracy of translation models in chat translation tasks.
%U https://aclanthology.org/2025.coling-main.221/
%P 3283-3299

Markdown (Informal)

[MQM-Chat: Multidimensional Quality Metrics for Chat Translation](https://aclanthology.org/2025.coling-main.221/) (Li et al., COLING 2025)

• MQM-Chat: Multidimensional Quality Metrics for Chat Translation (Li et al., COLING 2025)

ACL

• Yunmeng Li, Jun Suzuki, Makoto Morishita, Kaori Abe, and Kentaro Inui. 2025. MQM-Chat: Multidimensional Quality Metrics for Chat Translation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 3283–3299, Abu Dhabi, UAE. Association for Computational Linguistics.