Survey of Research on End-to-End Emotional Dialogue Generation

Abstract

Human-machine dialogue, an important research component of artificial intelligence, has received widespread attention from academia and industry. Inspired by the successful application of deep learning in natural language processing, a growing number of neural network models are being focused on by researchers. Among them, end-to-end based neural network models are able to learn valuable patterns and features from large-scale corpus to generate meaningful and diverse responses, and are widely used in research on emotional dialogue generation. This paper presents a review of the research on end-to-end models for emotional dialogue generation. Firstly, the tasks and main problems addressed by current research on emotional dialogue generation are outlined and defined in detail in the light of existing research results. The datasets required for modeling emotional dialogue generation models are organized and presented. Secondly, a brief overview of the principles of end-to-end neural network models is given, and the improvements in each of the underlying models, the current state of research, the evaluation metrics involved in the models, and the performance of the models are analyzed and summarized. Thirdly, the evaluation methods involved in the current stage of model evaluation are summarized in terms of automatic evaluation as well as manual evaluation. Finally, this paper prospects the development direction of the research on the generation of emotional dialogue in the future.

Keywords

• |human-machine dialogue|deep learning|end-to-end|emotional dialogue generation