Semantic Parsing with Neural Hybrid Trees

Abstract

We propose a neural graphical model for parsing natural language sentences into their logical representations. The graphical model is based on hybrid tree structures that jointly represent both sentences and semantics. Learning and decoding are done using efficient dynamic programming algorithms. The model is trained under a discriminative setting, which allows us to incorporate a rich set of features. Hybrid tree structures have shown to achieve state-of-the-art results on standard semantic parsing datasets. In this work, we propose a novel model that incorporates a rich, nonlinear featurization by a feedforward neural network. The error signals are computed with respect to the conditional random fields (CRFs) objective using an inside-outside algorithm, which are then backpropagated to the neural network. We demonstrate that by combining the strengths of the exact global inference in the hybrid tree models and the power of neural networks to extract high level features, our model is able to achieve new state-of-the-art results on standard benchmark datasets across different languages.