Our submission IR_MultiagentMT to the WMT25 General MT Task was presented at WMT 2025, together with our paper on document-level MT evaluation, which was accepted as a main research paper.
Multi-agentMT: Deploying AI Agent in the WMT25 Shared Task
Abstract Paper Code BibTeX
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.
A Preliminary Study of AI Agent Model in Machine Translation
Abstract Paper Code BibTeX
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.
Context is Ubiquitous, but Rarely Changes Judgments: Revisiting Document-Level MT Evaluation
Abstract Paper Code BibTeX
As sentence-level performance in modern Machine Translation (MT) has plateaued,reliable document-level evaluation is increasingly needed.While the recent FALCON framework with pragmatic features offers a promising direction, its reliability and reproducibility are unclear.We address this gap through human evaluation,analyzing sources of low inter-annotator agreement and identifying key factors. Based on these findings,we introduce H-FALCON,a Human-centered refinement of FALCON. Our experiments show that, even with limited annotator consensus,H-FALCON achieves correlations comparable to or better than standard sentence-level protocols. Furthermore, we find that contextual information is inherent in all sentences, challenging the view that only some require it. This suggests that prior estimates such as “n% of sentences require context” may stem from methodological artifacts. At the same time,we show that while context is pervasive,not all of it directly influences human judgment.