Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics - Lin, Chin-Yew; Och, Franz Josef (The 42nd Annual Meeting on Association for Computational Linguistics (ACL'04), 2004)
In this paper we describe two new objective automatic evaluation methods for machine translation. The first method is based on longest common subsequence between a candidate translation and a set of reference translations. Longest common subsequence takes into account sentence level structure similarity naturally and identifies longest co-occurring insequence n-grams automatically. The second method relaxes strict n-gram matching to skipbigram matching. Skip-bigram is any pair of words in their sentence order. Skip-bigram cooccurrence statistics measure the overlap of skip-bigrams between a candidate translation and a set of reference translations. The empirical results show that both methods correlate with human judgments very well in both adequacy and fluency.