Namely, BLEU scores are compared to reference human translations, which differ from translator to translator. And a concise hypothesis of the length 12. I said approximately because the original ROUGE implementation from the paper that introduced ROUGE {3} may perform a few more things such as stemming. And Sun (2010) compared three different metrics―BLEU, GTM and TER―and again found that BLEU scores were the least closely correlated with human judgements. 02/21/2019; 2 minutes to read +1; In this article. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and remains one of the most popular automated and inexpensive metrics. So the BLEU score was due to Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. BLEU (Bilingual Evaluation Understudy) is a measurement of the differences between an automatic translation and one or more human-created reference translations of the same source sentence. Xiamen, China. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU. A score greater than 50% is a very good score; significantly less post-editing will be required to achieve publishable translation quality. What is a BLEU score? BLEU rewards translations that have large overlap with human translations of sentences, with some extra heuristics thrown in to guard against weird pathologies (like full sentences getting translated as one word, redundancies, and repetition). The BLEU algorithm compares consecutive phrases of the automatic translation with the consecutive phrases it … Improving BLEU Score. A score greater than 50% is a very good score and significantly less post-editing will be required to achieve publishable translation quality. Finally, Finally, let's put this together to form the final BLEU score. References BLEU: a Method for Automatic Evaluation of Machine Translation Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu IBM T. J. Watson Research Center Yorktown Heights, NY 10598, USA fpapineni,roukos,toddward,weijingg@us.ibm.com However, it still is a good metric, used to evaluate machine translation performance up until now. There is a high correlation between the number of words used in training a KantanMT engine and its BLEU score. There are three references with length 12, 15 and 17. ROUGE-n F1-score=40% is more difficult to interpret, like any F1-score. I’m not the only one with reservations As the selected translation for each segment may not be the only correct one, it is often possible to score good translations poorly. BLEU might produce relatively good score to bad translation. ROUGE is more interpretable than BLEU (from {2}: "Other Known Deficiencies of Bleu: Scores hard to interpret"). As the selected translation for each segment may not be the only correct one, it is often possible to score good translations poorly. Therefore, BLEU scores give a general sense of how good translation is, but will never be a perfect assessment of translation quality. For example, if the n-gram precision score is 0.0442 and the brevity penalty is 0.8356, the final BLEU score is 0.0442 x 0.8356 = 0.0370. President, Safaba Translation Solutions . An example from the paper. Improving BLEU Score. BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. I will try to shed light on each of the questions. In other words: if you want humans to enjoy using your system, you shouldn’t just be focusing on getting a higher BLEU score. Therefore, iBLEU reports 0.0370 as 3.70. This is why it is commonly referred to as a Recall and Precision measurement. President, Association for MT in the Americas (AMTA) 13th MT Summit Tutorial. Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good … You can calculate BLEU score using the BLEU module under nltk.See here.. From there you can easily compute the alignment score between the candidate and reference sentences.