Paraphrase identification as probabilistic quasi-synchronous recognition

Dipanjan Das, Noah A. Smith
2009 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - ACL-IJCNLP '09   unpublished
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
more » ... ine 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.
doi:10.3115/1687878.1687944 fatcat:4qgubtkgjjd7dc33mfzguwdsui