Infusing Emotions into Task-oriented Dialogue Systems: Understanding, Management, and Generation
This work addresses the lack of emotion modeling in task-oriented dialogue systems, potentially improving user satisfaction and effectiveness in applications like customer service or virtual assistants, though it builds incrementally on existing datasets and concepts.
The authors tackled the problem of integrating emotions into task-oriented dialogue systems, which typically focus only on task success, by incorporating emotion understanding, management, and generation into a complete processing loop. They demonstrated that their framework significantly enhances both the user's emotional experience and task success through interactive experiments with simulated and human users.
Emotions are indispensable in human communication, but are often overlooked in task-oriented dialogue (ToD) modelling, where the task success is the primary focus. While existing works have explored user emotions or similar concepts in some ToD tasks, none has so far included emotion modelling into a fully-fledged ToD system nor conducted interaction with human or simulated users. In this work, we incorporate emotion into the complete ToD processing loop, involving understanding, management, and generation. To this end, we extend the EmoWOZ dataset (Feng et al., 2022) with system affective behaviour labels. Through interactive experimentation involving both simulated and human users, we demonstrate that our proposed framework significantly enhances the user's emotional experience as well as the task success.