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    This study introduces a language-independent fuzzy-match repair (FMR) method for computer-aided translation. The approach enhances translation memory tools by using machine translation and quality estimation to improve segment repair accuracy.

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    Area of Science:

    • Computational Linguistics
    • Natural Language Processing
    • Machine Translation

    Background:

    • Computer-aided translation (CAT) tools leverage translation memories (TMs) containing source-target segment pairs.
    • Professional translators use TMs to find similar past translations for new source segments.
    • Fuzzy-match repair (FMR) techniques aim to automatically adapt existing target segments for new translations.

    Purpose of the Study:

    • To develop and evaluate a language-independent fuzzy-match repair (FMR) method for CAT tools.
    • To improve the quality of automatically repaired translations from translation memories.
    • To assess the performance of a unified quality estimation model across multiple languages.

    Main Methods:

    • A novel language-independent FMR method was developed using machine translation (MT).
    • Candidate repaired segments are generated based on the source segment and a retrieved TM entry.
    • A quality estimation model selects the best candidate, approximating oracle quality.

    Main Results:

    • The proposed FMR method produced selected candidates that closely approximated the best possible (oracle) repairs.
    • Repaired segments were significantly closer to reference translations than raw MT outputs or unrepaired fuzzy matches.
    • A single quality estimation model demonstrated robust performance across three different language pairs.

    Conclusions:

    • The developed FMR technique effectively enhances translation memory utilization in CAT tools.
    • The language-independent approach and unified quality estimation offer a scalable solution for diverse language pairs.
    • This method provides a valuable tool for improving translation efficiency and quality.