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End-to-end learning framework is useful for building dialog systems for itssimplicity in training and efficiency in model updating. However, currentend-to-end approaches only consider user semantic inputs in learning andunder-utilize other user information. Therefore, we propose to include usersentiment obtained through multimodal information (acoustic, dialogic andtextual), in the end-to-end learning framework to make systems moreuser-adaptive and effective. We incorporated user sentiment information in bothsupervised and reinforcement learning settings. In both settings, addingsentiment information reduced the dialog length and improved the task successrate on a bus information search task. This work is the first attempt toincorporate multimodal user information in the adaptive end-to-end dialogsystem training framework and attained state-of-the-art performance.

Sentiment Adaptive End-to-End Dialog Systems