@inproceedings{sharma-2025-quality,
    title = "Quality-Informed Segment-Level Error Correction Using Natural Language Explanations from x{T}ower and Large Language Models",
    author = "Sharma, Prashant",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.75/",
    pages = "999--1003",
    ISBN = "979-8-89176-341-8",
    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{\textrightarrow}ZH, EN{\textrightarrow}CS, EN{\textrightarrow}IS, EN{\textrightarrow}JA, EN{\textrightarrow}RU, EN{\textrightarrow}UK), our approach demonstrates promising results, especially in cases requiring fine-grained edits."
}