Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks

TL;DR

This paper proposes K-SAN, a knowledge-guided structural attention network that incorporates prior linguistic knowledge into RNNs to better capture substructures in language, improving semantic understanding in NLU tasks.

Contribution

Introduces K-SAN, a novel model that integrates structured prior knowledge into neural networks for enhanced natural language understanding.

Findings

K-SAN outperforms state-of-the-art neural models on ATIS dataset.

The model effectively captures salient substructures with limited training data.

K-SAN automatically identifies important linguistic substructures.

Abstract

Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently recurrent neural networks (RNN) obtained strong results on NLU due to their superior ability of preserving sequential information over time. Traditionally, the NLU module tags semantic slots for utterances considering their flat structures, as the underlying RNN structure is a linear chain. However, natural language exhibits linguistic properties that provide rich, structured information for better understanding. This paper introduces a novel model, knowledge-guided structural attention networks (K-SAN), a generalization of RNN to additionally incorporate non-flat network topologies guided by prior knowledge. There are two characteristics: 1) important substructures can be captured from small training data, allowing the model to generalize to previously unseen test data; 2) the model automatically figures out the salient substructures that are essential to predict the semantic tags of the given sentences, so that the understanding performance can be improved. The experiments on the benchmark Air Travel Information System (ATIS) data show that the proposed K-SAN architecture can effectively extract salient knowledge from substructures with an attention mechanism, and outperform the performance of the state-of-the-art neural network based frameworks.