Exploiting discourse information to identify paraphrases

Previous work on paraphrase identification using sentence similarities has not exploited discourse structures, which have been shown as important information for paraphrase computation. In this paper, we propose a new method named EDU-based similarity, to compute the similarity between two sentences based on elementary discourse units. Unlike conventional methods, which directly compute similarities based on sentences, our method divides sentences into discourse units and employs them to compute similarities. We also show the relation between paraphrases and discourse units, which plays an important role in paraphrasing. We apply our method to the paraphrase identification task. Experimental results on the PAN corpus, a large corpus for detecting paraphrases, show the effectiveness of using discourse information for identifying paraphrases. We achieve 93.1% and 93.4% accuracy, respectively by using a single SVM classifier and by using a maximal voting model.