A Deep Learning Methodology for Semantic Utterance Classification in Virtual Human Dialogue Systems

摘要

This paper describes the development of a deep learning methodology for semantic utterance classification (SUC) for use in domain-specific dialogue systems. Semantic classifiers need to account for a variety of instances where the utterance for the semantic domain class varies. In order to capture the candidate relationships between the semantic class and the word sequence in an utterance, we have proposed a shallow convolutional neural network (CNN) along with a recurrent neural network (RNN) that uses domain-specific word embeddings which have been initialized using Word2Vec for determining semantic similarity of words. Experimental results demonstrate the effectiveness of shallow neural networks for SUC.