2002

BLUE: A Method for Automatic Evaluation of Machine Translation

K. Papineni, S. Roukos, T. Ward, Wei Jing Zhu

citations

Cite Score

95

AI summary

This paper introduces BLEU, a method for automatic machine translation evaluation that is quick, inexpensive, and language-independent. BLEU correlates highly with human evaluation and has little marginal cost per run, serving as an automated understudy to skilled human judges.

Main Contributions

  • Introduces the BLEU metric for automatic machine translation evaluation.
  • BLEU is quick, inexpensive, and language-independent.
  • BLEU correlates highly with human evaluation.
  • BLEU uses a modified n-gram precision to capture adequacy and fluency.
  • The BLEU metric ranges from 0 to 1 and correlates well with human judgement.

Abstract

Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

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References [4]

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F. Reeder - 2001

1 paper in library cites

K. Papineni, S. Roukos, T. Ward, J. Henderson, F. Reeder - 2002

1 paper in library cites

J. S. White, T. O'connell - 1994

1 paper in library cites

Eduard Hovy - 1999

1 paper in library cites

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