Sentiment Adaptive End-to-End Dialog Systems
This work addresses the need for more adaptive and effective dialog systems for users, though it is incremental as it builds on existing end-to-end frameworks.
The paper tackled the problem of under-utilizing user information in end-to-end dialog systems by incorporating multimodal sentiment analysis, resulting in reduced dialog length and improved task success rate on a bus information search task.
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.