@inproceedings{man-etal-2026-multi,
title = "Can Multi-agent Help Disambiguation in Multi-domain Translation?",
author = "Man, Zhibo and
Xu, Shaoyang and
Zhang, Yujie and
Feng, Yi and
Chen, Yuanmeng and
Chen, Yufeng and
Jinan, Xu and
Zhang, Wenxuan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.907/",
pages = "18234--18244",
ISBN = "979-8-89176-395-1",
abstract = "Large language models (LLMs)-based multi-agent systems have recently shown strong potential for machine translation (MT). However, their application to multi-domain translation (MDT) remains under-explored, particularly in addressing cross-domain word ambiguity. To investigate whether multi-agent approaches can help disambiguation in MDT, we propose a multi-agent collaborative disambiguation framework for MDT (MACD), which leverages the collaborative capabilities of LLMs for disambiguation. MACD consists of four cooperating agents responsible for domain allocation, general translation, domain disambiguation, and translation fusion. Experimental results show that MACD significantly improves translation performance across multiple domains and enhances disambiguation accuracy. Our approach reveals several findings on multi-agent collaboration in resolving word ambiguities."
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<abstract>Large language models (LLMs)-based multi-agent systems have recently shown strong potential for machine translation (MT). However, their application to multi-domain translation (MDT) remains under-explored, particularly in addressing cross-domain word ambiguity. To investigate whether multi-agent approaches can help disambiguation in MDT, we propose a multi-agent collaborative disambiguation framework for MDT (MACD), which leverages the collaborative capabilities of LLMs for disambiguation. MACD consists of four cooperating agents responsible for domain allocation, general translation, domain disambiguation, and translation fusion. Experimental results show that MACD significantly improves translation performance across multiple domains and enhances disambiguation accuracy. Our approach reveals several findings on multi-agent collaboration in resolving word ambiguities.</abstract>
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%0 Conference Proceedings
%T Can Multi-agent Help Disambiguation in Multi-domain Translation?
%A Man, Zhibo
%A Xu, Shaoyang
%A Zhang, Yujie
%A Feng, Yi
%A Chen, Yuanmeng
%A Chen, Yufeng
%A Jinan, Xu
%A Zhang, Wenxuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F man-etal-2026-multi
%X Large language models (LLMs)-based multi-agent systems have recently shown strong potential for machine translation (MT). However, their application to multi-domain translation (MDT) remains under-explored, particularly in addressing cross-domain word ambiguity. To investigate whether multi-agent approaches can help disambiguation in MDT, we propose a multi-agent collaborative disambiguation framework for MDT (MACD), which leverages the collaborative capabilities of LLMs for disambiguation. MACD consists of four cooperating agents responsible for domain allocation, general translation, domain disambiguation, and translation fusion. Experimental results show that MACD significantly improves translation performance across multiple domains and enhances disambiguation accuracy. Our approach reveals several findings on multi-agent collaboration in resolving word ambiguities.
%U https://aclanthology.org/2026.findings-acl.907/
%P 18234-18244
Markdown (Informal)
[Can Multi-agent Help Disambiguation in Multi-domain Translation?](https://aclanthology.org/2026.findings-acl.907/) (Man et al., Findings 2026)
ACL
- Zhibo Man, Shaoyang Xu, Yujie Zhang, Yi Feng, Yuanmeng Chen, Yufeng Chen, Xu Jinan, and Wenxuan Zhang. 2026. Can Multi-agent Help Disambiguation in Multi-domain Translation?. In Findings of the Association for Computational Linguistics: ACL 2026, pages 18234–18244, San Diego, California, United States. Association for Computational Linguistics.