Sentiment Adaptive End-to-End Dialog Systems

Weiyan Shi, Zhou Yu
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
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
more » ... 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.
doi:10.18653/v1/p18-1140 dblp:conf/acl/YuS18 fatcat:g6qahcdmcregvkrvf4b4ntzyhu