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The widely-used automatic evaluation metrics cannot adequately reflect thefluency of the translations. The n-gram-based metrics, like BLEU, limit themaximum length of matched fragments to n and cannot catch the matched fragmentslonger than n, so they can only reflect the fluency indirectly. METEOR, whichis not limited by n-gram, uses the number of matched chunks but it does notconsider the length of each chunk. In this paper, we propose an entropy-basedmethod, which can sufficiently reflect the fluency of translations through thedistribution of matched words. This method can easily combine with thewidely-used automatic evaluation metrics to improve the evaluation of fluency.Experiments show that the correlations of BLEU and METEOR are improved onsentence level after combining with the entropy-based method on WMT 2010 andWMT 2012.

Improve the Evaluation of Fluency Using Entropy for Machine Translation Evaluation Metrics