A Unified Approach to Emotion Detection and Task-Oriented Dialogue Modeling

Emotional user utterances frequently occur during interactions with dialogue systems [19]. In open-domain dialogues, users directly share personal and emotional experiences [10]. In task-oriented dialogues (TODs), emotions are closely related to task progression. They usually become apparent as the task unfolds, and are contingent on the user's expectations being met [5]. Detecting these emotions explicitly in either scenario is beneficial for several reasons: it can help with reviewing chat logs after the exchange concludes or with adjusting responses during the exchange to better align with the user's emotional state [18]. In TODs, these more empathetic responses have demonstrated their effectiveness in compensating for system errors [12], creating the impression of a more capable system. Existing text-based approaches to ED in TODs either require a dedicated, specifically trained component [5, 6] or assume implicit ED. This assumption is due to systems being commonly trained to replicate human expert responses [25, 7] which inherently convey empathy, as needed, and therefore implicit ED. In contrast, we propose an approach that eliminates the need for training an additional component while explicitly modeling user emotions, applicable in settings where emotion annotations are available.