Semantic Parsing with Relaxed Hybrid Trees

Abstract

We propose a novel model for parsing natural language sentences into their formal semantic representations. The model is able to perform integrated lexicon acquisition and semantic parsing, mapping each atomic element in a complete semantic representation to a contiguous word sequence in the input sentence in a recursive manner, where certain overlappings amongst such word sequences are allowed. It defines distributions over the novel relaxed hybrid tree structures which jointly represent both sentences and semantics. Such structures allow tractable dynamic programming algorithms to be developed for efficient learning and decoding. Trained under a discriminative setting, our model is able to incorporate a rich set of features where certain unbounded long-distance dependencies can be captured in a principled manner. We demonstrate through experiments that by exploiting a large collection of simple features, our model is shown to be competitive to previous works and achieves state-of-theart performance on standard benchmark data across four different languages. The system and code can be downloaded from http://statnlp.org/research/sp/.