Paper: Automatic Evaluation Of Machine Translation Quality Using Longest Common Subsequence And Skip-Bigram Statistics

ACL ID P04-1077
Title Automatic Evaluation Of Machine Translation Quality Using Longest Common Subsequence And Skip-Bigram Statistics
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

In this paper we describe two new objective automatic evaluation methods for machine translation. The first method is based on long- est common subsequence between a candidate translation and a set of reference translations. Longest common subsequence takes into ac- count sentence level structure similarity natu- rally and identifies longest co-occurring in- sequence n-grams automatically. The second method relaxes strict n-gram matching to skip- bigram matching. Skip-bigram is any pair of words in their sentence order. Skip-bigram co- occurrence statistics measure the overlap of skip-bigrams between a candidate translation and a set of reference translations. The empiri- cal results show that both methods correlate with human judgments very well in both ade- quacy and fluency.