Date : 21 January 2016
Speaker: Shashi Narayan
Title: Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
Abstract: One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries -- there are many ways to ask a question, all with the same answer. In this paper we propose to bridge this gap by generating paraphrases to the input question with the goal that at least one of them will be correctly mapped to a correct knowledge-base query. We introduce a novel grammar model for paraphrase generation that does not require any sentence-aligned paraphrase corpus. Our key idea is to leverage the flexibility and scalability of latent-variable probabilistic context-free grammars to sample paraphrases. We do an extrinsic evaluation of our paraphrases by plugging them into a semantic parser for Freebase. Our evaluation experiments on WebQuestions benchmark dataset show that the performance of the semantic parser significantly improves over strong baselines.
Bio: Shashi Narayan is a research associate at School of Informatics at the University of Edinburgh. He is currently working with Shay Cohen on the problems of spectral methods for parsing and generation. Before, he earned his doctoral degree in 2014 from Université de Lorraine, under the supervision of Claire Gardent. He received Erasmus Mundus Masters scholarship (2009-2011) in Language and Communication Technology (EM-LCT). He did his major (Bachelor of Technology (Honors), 2005-2009) in Computer Science and Engineering from Indian Institute of Technology (IIT), Kharagpur India.
He is interested in the application of syntax and semantics to solve various NLP problems, in particular, natural language generation, parsing, sentence simplification, paraphrase generation and question answering.