The task of machine translation (MT) evaluation is closely related to the task of sentence-level semantic equivalence classification. This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predict semantic equivalence and entailment. We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. Our results show that MT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extent entailment. Our technique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments.
In addition, REF applied MT evaluation methods (BLEU, NIST, WER and PER) to build classifiers for paraphrase identification.
Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence
Proceedings of the Third International Workshop on Paraphrasing (IWP2005)
Computer Science