Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics (2004)
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BibTeX
@MISC{Lin04automaticevaluation,
author = {Chin-Yew Lin and et al.},
title = {Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics},
year = {2004}
}
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Abstract
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.
