Eliciting Positive Emotion through Affect-Sensitive Dialogue Response Generation: A Neural Network Approach

Abstract

An emotionally-competent computer agent could be a valuable assistive technology in performing various affective tasks. For example caring for the elderly, low-cost ubiquitous chat therapy, and providing emotional support in general, by promoting a more positive emotional state through dialogue system interaction. However, despite the increase of interest in this task, existing works face a number of shortcomings: system scalability, restrictive modeling, and weak emphasis on maximizing user emotional experience. In this paper, we build a fully data driven chat-oriented dialogue system that can dynamically mimic affective human interactions by utilizing a neural network architecture. In particular, we propose a sequence-to-sequence response generator that considers the emotional context of the dialogue. An emotion encoder is trained jointly with the entire network to encode and maintain the emotional context throughout the dialogue. The encoded emotion information is then incorporated in the response generation process. We train the network with a dialogue corpus that contains positive-emotion eliciting responses, collected through crowd-sourcing. Objective evaluation shows that incorporation of emotion into the training process helps reduce the perplexity of the generated responses, even when a small dataset is used. Subsequent subjective evaluation shows that the proposed method produces responses that are more natural and likely to elicit a more positive emotion.