  Emotional support is a crucial skill for many real-world scenarios, including
caring for the elderly, mental health support, and customer service chats. This
paper presents a novel task of empathetic dialog generation with positive
emotion elicitation to promote users' positive emotions, similar to that of
emotional support between humans. In this task, the agent conducts empathetic
responses along with the target of eliciting the user's positive emotions in
the multi-turn dialog. To facilitate the study of this task, we collect a
large-scale emotional dialog dataset with positive emotion elicitation, called
PosEmoDial (about 820k dialogs, 3M utterances). In these dialogs, the agent
tries to guide the user from any possible initial emotional state, e.g.,
sadness, to a positive emotional state. Then we present a
positive-emotion-guided dialog generation model with a novel loss function
design. This loss function encourages the dialog model to not only elicit
positive emotions from users but also ensure smooth emotional transitions along
with the whole dialog. Finally, we establish benchmark results on PosEmoDial,
and we will release this dataset and related source code to facilitate future
studies.
