Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics

  • Authors:
  • Chin-Yew Lin;Franz Josef Och

  • Affiliations:
  • University of Southern California, Marina del Rey, CA;University of Southern California, Marina del Rey, CA

  • Venue:
  • ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

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 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 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.