Quality-Informed Segment-Level Error Correction Using Natural Language Explanations from x T ower and Large Language Models (2025)

Abstract: This paper describes our submission to the WMT25 Automated Translation Quality Evaluation Systems Task 3 - QE-informed Segment-level Error Correction. We propose a two-step approach for Automatic Post-Editing (APE) that leverages natural language explanations of translation errors. Our method first utilises the xTower model to generate a descriptive explanation of the errors present in a machine-translated segment, given the source text, the machine translation, and quality estimation annotations. This explanation is then provided as a prompt to a powerful Large Language Model, Gemini 1.5 Pro, which generates the final, corrected translation. This approach is inspired by recent work in edit-based APE and aims to improve the interpretability and performance of APE systems. We Evaluated across six language pairs (EN→ZH, EN→CS, EN→IS, EN→JA, EN→RU, EN→UK), our approach demonstrates promising results, especially in cases requiring fine-grained edits.

## introduction machine translation (mt) has undergone rapid development in recent years, largely driven by the success of neural machine translation (nmt) models. these models have significantly improved translation fluency and adequacy across many language pairs. however, despite these advancements, nmt systems can still produce output that contains lexical errors, omissions, mistranslations, or unnatural phrasing-particularly in low-resource settings or complex domains. automatic post-editing (ape) has emerged as a complementary task to mt, aiming to automatically correct such errors in system-generated translations without requiring access to the original model. ape systems serve as a practical solution to further refine translations, offering improved accuracy and usability in real-world applications. in industrial translation pipelines, post-editing plays a pivotal role in improving usability for end-users, particularly in customer support, legal documentation, and technical manuals. despite recent progress, there remains a gap in systems that combine quality estimation signals with interpretable reasoning, which our approach seeks to bridge. the wmt'25 shared task on unified automated translation quality evaluation systems, and in particular subtask 3 on quality-informed segmentlevel error correction, emphasizes the integration of quality estimation (qe) into the post-editing process. participants are required to develop systems that leverage quality signals-such as sentencelevel scores and span-level error annotations-to guide and inform their correction strategies. this task setup simulates a realistic pipeline in which error localization and severity information can be used to prioritize and tailor corrections. our approach to this challenge is inspired by the "detector-corrector" architecture proposed by @xcite , which separates the tasks of error identification and correction. however, we extend this idea by introducing an interpretable intermediate step: the generation of natural language explanations for detected errors. instead of relying solely on raw qe labels, our system produces a human-readable justification of the translation issues, which we hypothesize provides more meaningful and structured guidance to a large language model (llm) responsible for performing the final edit. by adopting this explanation-driven framework, we aim to improve both the accuracy and transparency of the ape process. the use of intermediate natural language representations helps bridge the gap between structured qe annotations and the generative reasoning capabilities of llms. our system builds on this intuition and comprises two main components: an explanation generation module based on the xtower model @xcite , and a correction module using gemini 1.5 pro @xcite . our proposed system is characterized by : ## proposed approach our proposed system is designed to perform quality-informed automatic post-editing by explicitly modeling the editing process as two semantically distinct stages: first, identifying and interpreting the errors in the translation; and second, applying appropriate corrections based on that understanding. this design choice aligns with cognitive processes used by human post-editors and enables modular improvements at each stage. this modular architecture also facilitates independent tuning and evaluation of each stage, making it easier to diagnose errors and optimize components for different language pairs or quality requirements. the overall architecture of our system is depicted in figure 1 . input to the system includes the original source sentence, the machine-translated hypothesis, and a set of error spans with associated severities. these inputs are first processed by xtower @xcite , a pretrained multilingual model fine-tuned for generating error explanations. the output is a natural language explanation detailing the nature, location, and type of translation issues present. ## experimental setup to assess the effectiveness of our proposed system, we conducted experiments wmt25 automated translation quality evaluation systems task 3 -qe-informed segment-level error correction. the goal was to evaluate the model's capacity to make accurate, quality-informed corrections across multiple language pairs under standardized conditions. ## results and analysis we will present the results of our experiments in this section. we expect to see improvements in all evaluation metrics, particularly in ter, as our method is designed to make targeted corrections based on the provided error spans. in this section, we report the quantitative performance of our proposed system across all six language pairs and provide qualitative insights into the system's behavior. our primary focus is on evaluating our systems using three metrics: bleu @xcite , ter @xcite , and comet @xcite . the results, presented in ## conclusion in this paper, we presented our system, a modular two-step system for quality-informed automatic post-editing (ape). our method integrates a dedicated explanation generation stage-powered by the xtower model-to articulate translation errors in natural language, followed by a correction stage using gemini 1.5 pro to generate high-quality postedits. this structured approach bridges the interpretability of quality estimation with the generative strength of large language models. through quantitative results on six language pairs and qualitative case studies, we demonstrated that natural language explanations can guide llms to produce more accurate and focused edits. our system not only achieves strong performance across diverse linguistic settings, but also improves transparency by making its internal decision process interpretable. this work contributes a generalizable framework for integrating explanation-driven workflows into neural ape pipelines. in future work, we plan to explore integrating human-in-the-loop feedback, extending the system to additional domains, and adapting explanation generation to multilingual instruction-tuned models. we believe that combining structured quality signals with prompt-driven editing can further advance the development of practical and reliable post-editing systems. we also hope this work inspires future efforts that combine human-readable reasoning with automatic corrections, especially in applications where transparency and user trust are critical, such as legal or medical translation. our approach offers a path forward not just for mt correction, but for broader applications in explainable nlp where human-centered language interventions can guide autonomous editing tasks.