articleJul 1, 2004Closed access

Statistical Significance Tests for Machine Translation Evaluation.

Massachusetts Institute of Technology

Abstract

If two translation systems differ differ in performance on a test set, can we trust that this indicates a difference in true system quality? To answer this question, we describe bootstrap resampling methods to compute statistical significance of test results, and validate them on the concrete example of the BLEU score. Even for small test sizes of only 300 sentences, our methods may give us assurances that test result differences are real.

Citation impact

1,467
total citations
FWCI
20.35
Percentile
100%
References
15
Citations per year

Authors

1

Topics & keywords

Keywords
  • Resampling
  • Machine translation
  • Computer science
  • Test (biology)
  • Translation (biology)
  • Artificial intelligence
  • Statistical significance
  • Natural language processing
UN Sustainable Development Goals
  • Quality Education
No related works found for this paper.