Dialogue intent classification with character-CNN-BGRU networks

Abstract

Dialogue intent classification plays a significant role in human-computer interaction systems. In this paper, we present a hybrid convolutional neural network and bidirectional gated recurrent unit neural network (CNN-BGRU) architecture to classify the intent of a dialogue utterance. First, character embeddings are trained and used as the inputs of the proposed model. Second, a CNN is used to extract local features from each utterance, and a maximum pooling layer is applied to select the most crucial latent semantic factors. A bidirectional gated recurrent unit (BGRU) layer architecture is used to capture the contextual semantic information. Then, two feature maps, which are the outputs of the two architectures, are integrated into the final utterance representation. The proposed model can utilize local semantic and contextual information to recognize and classify the user dialogue intent in an efficient way. The proposed model is evaluated based on a social media processing (SMP) data set and a real conversational data set. The experimental results show that the proposed model outperforms the corresponding traditional methods. In addition, compared to the CNN and BGRU methods, the classification accuracy of the proposed model is 1.4% higher for the SMP data set.