Paper: Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition

ACL ID P09-1053
Title Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

We present a novel approach to decid- ing whether two sentences hold a para- phrase relationship. We employ a gen- erative model that generates a paraphrase of a given sentence, and we use proba- bilistic inference to reason about whether two sentences share the paraphrase rela- tionship. 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 comple- mentary 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.