Sentiment Adaptive End to End Dialog Systems
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 to end dialog system training framework and attained state of the art performance.
ACL