Multi-agentMT: Deploying AI Agent in the WMT25 Shared Task

Ahrii Kim


Abstract
We present Multi-agentMT, our system for the WMT25 General Shared Task. The model adopts Prompt Chaining, a multi-agent workflow combined with Rubric-MQM, an automatic MQM-based error annotation metric. Our primary submission follows a Translate–Postedit–Proofread pipeline, in which error positions are explicitly marked and iteratively refined. Results suggest that a semi-autonomous agent scheme for machine translation is feasible with a smaller, earlier-generation model in low-resource settings, achieving comparable quality at roughly half the cost of larger systems.
Anthology ID:
2025.wmt-1.53
Volume:
Proceedings of the Tenth Conference on Machine Translation
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venue:
WMT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
769–777
Language:
URL:
https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.53/
DOI:
Bibkey:
Cite (ACL):
Ahrii Kim. 2025. Multi-agentMT: Deploying AI Agent in the WMT25 Shared Task. In Proceedings of the Tenth Conference on Machine Translation, pages 769–777, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Multi-agentMT: Deploying AI Agent in the WMT25 Shared Task (Kim, WMT 2025)
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PDF:
https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.53.pdf