Paper: Semantic Parsing via Paraphrasing

ACL ID P14-1133
Title Semantic Parsing via Paraphrasing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

A central challenge in semantic parsing is handling the myriad ways in which knowl- edge base predicates can be expressed. Traditionally, semantic parsers are trained primarily from text paired with knowledge base information. Our goal is to exploit the much larger amounts of raw text not tied to any knowledge base. In this pa- per, we turn semantic parsing on its head. Given an input utterance, we first use a simple method to deterministically gener- ate a set of candidate logical forms with a canonical realization in natural language for each. Then, we use a paraphrase model to choose the realization that best para- phrases the input, and output the corre- sponding logical form. We present two simple paraphrase models, an association model and a vector space model, and train them jointly...