Sentiment Adaptive End-to-End Dialog Systems
Weiyan Shi, Zhou Yu · 2018
End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating.However, current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information.Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective.We incorporated user sentiment information in both supervised and reinforcement learning settings.In both settings, adding sentiment information reduced the dialog length and improved the task success rate on a bus information search task.This work is the first attempt to incorporate multimodal user information in the adaptive end-toend dialog system training framework and attained state-of-the-art performance.