(2002) Links and resources BibTeX key: … Automatic metrics are fundamental for the development and evaluation of machine translation systems. We will measure the closeness of translation by finding legitimate differences in word choice and word order between the reference human translation and translation generated by the machine. That said, there are a lot of automatic evaluation metrics that can be alternatives to BLEU. Papineni, K., et al.
Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, July 2002, pp. copy delete add this publication to your clipboard. Automatic metrics are fundamental for the development and evaluation of machine translation systems. translations whose length differs significantly from that of the reference translations. close.

For tasks like machine translation, for example, I personally think penalizing large changes in the meaning is very important.
Judging whether, and to what extent, automatic metrics concur with the gold standard of human evaluation is not a straightforward … In: Proceedings of the 40th Annual Meeting on ACL, pp. The closer a machine translation is to a professional human translation, the better it is : BLEU: a Method for Automatic Evaluation of Machine Translation by Kishore Papineni. def method2 (self, p_n, * args, ** kwargs): """ Smoothing method 2: Add 1 to both numerator and denominator from Chin-Yew Lin and Franz Josef Och (2004) Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. The BLEU score was proposed by Kishore Papineni, et al. 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. The approach works by counting matching n-grams in the candidate translation to n-grams in the reference text, where 1-gram or unigram would be each token and a bigram comparison would be each word pair. : BLEU: a method for automatic evaluation of machine translation. 311–318. 06/11/2020 ∙ by Nitika Mathur, et al. 311–318. With this problem in mind, scientists Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu presented a new method for the automatic evaluation of machine translation (MT), which would be faster, “inexpensive, and language-independent.” They called this the Bilingual Evaluation Understudy; or simply, BLEU.

Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics. We present the results of an experiment on extending the automatic method of Machine Translation evaluation BLUE with statistical weights for lexical items, such as tf.idf scores. We show that current methods for judging metrics are highly sensitive to the translations used for assessment, particularly the presence of outliers, …
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