@inproceedings{lardilleux-lepage-2017-charcut,
    title = "{CHARCUT}: Human-Targeted Character-Based {MT} Evaluation with Loose Differences",
    author = "Lardilleux, Adrien  and
      Lepage, Yves",
    booktitle = "Proceedings of the 14th International Conference on Spoken Language Translation",
    month = dec # " 14-15",
    year = "2017",
    address = "Tokyo, Japan",
    publisher = "International Workshop on Spoken Language Translation",
    url = "https://aclanthology.org/2017.iwslt-1.20",
    pages = "146--153",
    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.",
}