Submission for WMT 25 Task 3 (2025)

Abstract: 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 ∆COMET scoreof 0.0201 and −0.0108, respectively, leading the first approach to achieve the winning position on the subtask leaderboard.

## introduction large language models (llms) have advanced the field of machine translation (mt), given their support for longer input context length and ability to generate text in a natural tone. however, translation quality is still limited for languages other than english and for domain-specific translations @xcite . evaluating mt output for quality is critical to understand the reliability and suitability of translation systems, and more importantly, to be able to perform accurate corrections. the wmt24 metrics shared task found that neural-based learned metrics like comet or xcomet were superior when evaluating llmgenerated translations, compared to the traditional statistical metrics like bleu, or chrf @xcite . as llms are typically trained on a large general dataset, performance in domain-specific mt can often fall short as they may not properly render key terminologies or stylistic conventions. as such, automatic post-editing (ape) is vital to fixing mt errors. however, they are prone to overcorrecting. one such example of mitigating over-correcting, proposed by @xcite , is to utilize word-level quality estimation (qe) to limit edits only on the specified error segments. despite recent efforts to reduce overcorrection @xcite , ape models still fall short of the required semantically coherent output. therefore, we ask the titular question -"can qe-informed re-translation help overcome mt errors?", and discuss two approaches as a comparative evaluation. this paper describes two participation systems, both utilizing pre-trained and open-source llms, for the wmt 2025 automated translation quality evaluation systems task 3 -qe-informed segment-level error correction. the primary approach leverages multiple llms for mt and selects the best output using qe. in the secondary approach, an llm is prompted to replace errorsegments in the provided mt. these error segments are identified by the explainable qe provided within the dataset. ## conclusion and future work this paper described two translation approaches submitted to the wmt 2025 qe-informed segment-level error correction task. the first approach used qe as a selector among multiple llm translation outputs, resulting in an overall ∆comet of 0.0201. the second approach utilized towerplus-9b exclusively to replace erroneous words in the mt by masking substrings highlighted in the error spans with a blank token, resulting in ∆comet of -0.0108. custom prompts were designed to instruct the model in correcting the translation, similar to a fill-in-theblank task. while the first approach showed positive improvements by using qe as a selector, it ultimately depends on the model and system selection. further exploration of system combinations could yield better performance. the second approach, which performed worse, corrected translations by filling in error segments in the mt. as future work, a two-model system could be explored: a smaller lm to suggest words or phrases for masked tokens via masked language modeling, and a larger llm to select the most suitable ones to produce a higher-quality translation.