We show that current methods for judging metrics are highly sensitive to the translations used for assessment, particularly the presence of outliers, … 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. We show that current methods for judging metrics are highly sensitive to the translations used for assessment, particularly the presence of outliers, … IBM's formula for calculating the score (which IBM has dubbed "BLEU"1) is 1 Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu (2001). This report may be downloaded from URL Judging whether, and to what extent, automatic metrics concur with the gold standard of human evaluation is not a straightforward problem.
For this reason, even a human translator will not necessarily score 1.
Human evaluations of machine translation are extensive but expensive.
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. BibTeX @INPROCEEDINGS{Thabet91bleu:a, author = {Iman Thabet and Bernd Ludwig and Frank-peter Schweinberger and Kerstin Bücher and Günther Görz}, title = {BLEU: a Method for Automatic Evaluation of Machine Translation}, booktitle = {In ACL 2002, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics}, year = {1991}, pages = {311--318}}
copy delete add this publication to your clipboard. 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. Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu. We show that current methods for judging metrics are highly sensitive to the translations used for assessment, particularly the presence of outliers, … 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. Automatic metrics are fundamental for the development and evaluation of machine translation systems.
The original paper "BLEU: a Method for Automatic Evaluation of Machine Translation" contains a couple of numbers on this: The BLEU metric ranges from 0 to 1. That said, there are a lot of automatic evaluation metrics that can be alternatives to BLEU. Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, July 2002, pp. Bleu: a Method for Automatic Evaluation of Machine Translation Kishore Papineni Salim Roukos Todd Ward Wei-Jing Zhu IBM T. J. Watson Research Center Yorktown Heights, NY 10598, USA fpapineni,roukos,toddward,weijingg@us.ibm.com Abstract Human evaluations of machine translation are extensive but expensive. Automatic metrics are fundamental for the development and evaluation of machine translation systems. Copy citation to your local clipboard. “Bleu: a Method for Automatic Evaluation of Machine Translation”. Automatic metrics are fundamental for the development and evaluation of machine translation systems. Kishore Papineni et al. 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. In: Proceedings of 40th Annual Meeting of the Association for Computational Linguistics.
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. BLEU: a Method for Automatic Evaluation of Machine Translation. Anthology ID: P02-1040 Volume: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics Month: July Year: 2002 Address: Philadelphia, Pennsylvania, USA For tasks like machine translation, for example, I personally think penalizing large changes in the meaning is very important. Human evaluations of machine translation are extensive but expensive. BLEU: a method for aut... × Publication title. BLEU: a Method for Automatic Evaluation of Machine Translation (2002) ... {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu}, title = {BLEU: a Method for Automatic Evaluation of Machine Translation}, booktitle ... Human evaluations of machine translation are extensive but expensive. Advertisement. 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. 06/11/2020 ∙ by Nitika Mathur, et al. in their 2002 paper “BLEU: a Method for Automatic Evaluation of Machine Translation“. ACL (2002) In: Proceedings of the 40th Annual Meeting on ACL, pp. : BLEU: a method for automatic evaluation of machine translation. It is important to note that the more reference translations per sentence there are, … “Human evaluations of machine translation are extensive but expensive…can take months to finish, and involve human labor that cannot be reused.” So began the abstract of a 2002 paper by a group of IBM Watson Research Center scientists. 311–318. Human evaluations can take months to nish and involve human labor …
311–318. close. B leu: a Method for Automatic Evaluation of Machine Translation.
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