Emotional Conversation Generation Based on a Bayesian Deep Neural Network

Abstract

The field of conversation generation using neural networks has attracted increasing attention from researchers for several years. However, traditional neural language models tend to generate a generic reply with poor semantic logic and no emotion. This article proposes an emotional conversation generation model based on a Bayesian deep neural network that can generate replies with rich emotions, clear themes, and diverse sentences. The topic and emotional keywords of the replies are pregenerated by introducing commonsense knowledge in the model. The reply is divided into multiple clauses, and then a multidimensional generator based on the transformer mechanism proposed in this article is used to iteratively generate clauses from two dimensions: sentence granularity and sentence structure. Subjective and objective experiments prove that compared with existing models, the proposed model effectively improves the semantic logic and emotional accuracy of replies. This model also significantly enhances the diversity of replies, largely overcoming the shortcomings of traditional models that generate safe replies.