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We propose the use of character n-gram F-score for automatic evaluation of machine translation output. Character ngrams have already been used as a part of more complex metrics, but their individual potential has not been investigated yet. We report system-level correlations with human rankings for 6-gram F1-score (CHRF) on the WMT12, WMT13 and WMT14 data as well as segment-level correlation for 6gram F1 (CHRF) and F3-scores (CHRF3) on WMT14 data for all available target languages. The results are very promising, especially for the CHRF3 score -for translation from English, this variant showed the highest segment-level correlations outperforming even the best metrics on the WMT14 shared evaluation task.
Maja Popović (Thu,) studied this question.