A semantic and emotion-based dual latent variable generation model for a dialogue system

With the development of intelligent agents pursuing humanisation, artificial intelligence must consider emotion, the most basic spiritual need in human interaction. Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses. However, selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response. We propose a semantic and emotion-based dual latent variable generation model (Dual-LVG) for dialogue systems, which is able to generate appropriate emotional responses without an emotional dictionary. Different from previous work, the conditional variational autoencoder (CVAE) adopts the standard transformer structure. Then, Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion. The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively. Moreover, the average attention mechanism is adopted to better extract semantic features at the sequence level, and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model. Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.