We present a novel approach to deciding whether two sentences hold a paraphrase relationship. We employ a generative model that generates a paraphrase of a given sentence, and we use probabilistic inference to reason about whether two sentences share the paraphrase relationship. The model cleanly incorporates both syntax and lexical semantics using quasi-synchronous dependency grammars (Smith and Eisner, 2006) . Furthermore, using a product of experts (Hinton, 2002) , we combine the model with a complementary logistic regression model based on state-of-the-art lexical overlap features. We evaluate our models on the task of distinguishing true paraphrase pairs from false ones on a standard corpus, giving competitive state-of-the-art performance.
REF design a model which incorporates both syntax and lexical semantics using dependency grammars.
Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition
ACL-IJCNLP
Computer Science