Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition

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.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Dipanjan Das, Noah A. Smith
• Keywords: ACL 2009, Computational Linguistics, Model, Paraphrase, Paraphrase Relationship