  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 of the input question with the goal that at least one of them will
be correctly mapped to a 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 the WebQuestions
benchmark dataset show that the performance of the semantic parser
significantly improves over strong baselines.
