A New Task for Predicting Emotions and Dialogue Strategies in Task-Oriented Dialogue

With a focus on task-oriented conversational agents, we introduce a new approach to make the generated response more relevant to the emotional context of the interlocutor. This approach aims to predict the labels of the agent’s next speaker turn, in order to condition the generated response and ensure its consistency to the user’s socio-emotional context. First, we propose a new formulation of this prediction task, based on the joint prediction of dialogue strategies and emotional labels associated with the next speaker turn. To handle this new task, we describe a new annotation protocol for task-oriented dialogue systems, that we implement on real customer-relationship interactions provided by a company. Lastly, we conduct an experiment using two approaches: a classification and a generation model. We evaluate them on the new prediction task using the annotated data, before discussing the results. As expected, we see that our classifier tends to predict safe, similar labels where the generator has a more diverse output in spite of its lower performance on traditional metrics.