  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.
