This paper describes Parmesan, our submission to the 2014 Workshop on Statistical Machine Translation (WMT) metrics task for evaluation English-to-Czech translation. We show that the Czech Meteor Paraphrase tables are so noisy that they actually can harm the performance of the metric. However, they can be very useful after extensive filtering in targeted paraphrasing of Czech reference sentences prior to the evaluation. Parmesan first performs targeted paraphrasing of reference sentences, then it computes the Meteor score using only the exact match on these new reference sentences. It shows significantly higher correlation with human judgment than Meteor on the WMT12 and WMT13 data.
However, its Czech paraphrase tables are so noisy (i.e. they contain pairs of nonparaphrastic expressions) that they actually harm the performance of the metric, as it can reward mistranslated and even untranslated words REF .
Parmesan: Meteor without Paraphrases with Paraphrased References
WMT@ACL
Computer Science