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        <title>CHARCUT: Human-Targeted Character-Based MT Evaluation with Loose Differences</title>
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        <namePart type="given">Adrien</namePart>
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            <title>Proceedings of the 14th International Conference on Spoken Language Translation</title>
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            <namePart type="given">Sakriani</namePart>
            <namePart type="family">Sakti</namePart>
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    <abstract>We present CHARCUT, a character-based machine translation evaluation metric derived from a human-targeted segment difference visualisation algorithm. It combines an iterative search for longest common substrings between the candidate and the reference translation with a simple length-based threshold, enabling loose differences that limit noisy character matches. Its main advantage is to produce scores that directly reflect human-readable string differences, making it a useful support tool for the manual analysis of MT output and its display to end users. Experiments on WMT16 metrics task data show that it is on par with the best “un-trained” metrics in terms of correlation with human judgement, well above BLEU and TER baselines, on both system and segment tasks.</abstract>
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%0 Conference Proceedings
%T CHARCUT: Human-Targeted Character-Based MT Evaluation with Loose Differences
%A Lardilleux, Adrien
%A Lepage, Yves
%Y Sakti, Sakriani
%Y Utiyama, Masao
%S Proceedings of the 14th International Conference on Spoken Language Translation
%D 2017
%8 dec 14 15
%I International Workshop on Spoken Language Translation
%C Tokyo, Japan
%F lardilleux-lepage-2017-charcut
%X We present CHARCUT, a character-based machine translation evaluation metric derived from a human-targeted segment difference visualisation algorithm. It combines an iterative search for longest common substrings between the candidate and the reference translation with a simple length-based threshold, enabling loose differences that limit noisy character matches. Its main advantage is to produce scores that directly reflect human-readable string differences, making it a useful support tool for the manual analysis of MT output and its display to end users. Experiments on WMT16 metrics task data show that it is on par with the best “un-trained” metrics in terms of correlation with human judgement, well above BLEU and TER baselines, on both system and segment tasks.
%U https://aclanthology.org/2017.iwslt-1.20
%P 146-153

Markdown (Informal)

[CHARCUT: Human-Targeted Character-Based MT Evaluation with Loose Differences](https://aclanthology.org/2017.iwslt-1.20) (Lardilleux & Lepage, IWSLT 2017)

• CHARCUT: Human-Targeted Character-Based MT Evaluation with Loose Differences (Lardilleux & Lepage, IWSLT 2017)

ACL

• Adrien Lardilleux and Yves Lepage. 2017. CHARCUT: Human-Targeted Character-Based MT Evaluation with Loose Differences. In Proceedings of the 14th International Conference on Spoken Language Translation, pages 146–153, Tokyo, Japan. International Workshop on Spoken Language Translation.