用QE指导重生成，提升翻译纠错效果。
Can QE-informed (Re)Translation lead to Error Correction?
- 基于QE提示，从多个大模型生成译文中选最优结果。
- 新方法在测试集上比基准提升0.0201分（Delta COMET）。
- 适合需要高精度翻译的场景，无需额外训练。
本文针对WMT 2025自动翻译质量评估任务3——基于质量估计（QE）的段级错误修正，提出两种无需训练的方法。一种是让不同大模型生成多个候选译文，选择质量最高的作为最终输出；另一种则根据QE提供的错误描述，指令大模型替换特定错误子串，采用条件启发式策略减少修改次数，以最大化收益与修改比。前者取得0.0201的Delta COMET分数，领先于后者（-0.0108），并赢得该子任务第一名。
原文摘要 · Abstract (English)
The paper presents two approaches submitted to the WMT 2025 Automated Translation Quality Evaluation Systems Task 3 - Quality Estimation (QE)-informed Segment-level Error Correction. While jointly training QE systems with Automatic Post-Editing (APE) has shown improved performance for both tasks, APE systems are still known to overcorrect the output of Machine Translation (MT), leading to a degradation in performance. We investigate a simple training-free approach - QE-informed Retranslation, and compare it with another within the same training-free paradigm. Our winning approach selects the highest-quality translation from multiple candidates generated by different LLMs. The second approach, more akin to APE, instructs an LLM to replace error substrings as specified in the provided QE explanation(s). A conditional heuristic was employed to minimise the number of edits, with the aim of maximising the Gain-to-Edit ratio. The two proposed approaches achieved a Delta COMET score of 0.0201 and -0.0108, respectively, leading the first approach to achieve the winning position on the subtask leaderboard.
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