Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence 
 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. 