  Emotional support conversation (ESC) aims to provide emotional support (ES)
to improve one's mental state. Existing works stay at fitting grounded
responses and responding strategies (e.g., question), which ignore the effect
on ES and lack explicit goals to guide emotional positive transition. To this
end, we introduce a new paradigm to formalize multi-turn ESC as a process of
positive emotion elicitation. Addressing this task requires finely adjusting
the elicitation intensity in ES as the conversation progresses while
maintaining conversational goals like coherence. In this paper, we propose
Supporter, a mixture-of-expert-based reinforcement learning model, and well
design ES and dialogue coherence rewards to guide policy's learning for
responding. Experiments verify the superiority of Supporter in achieving
positive emotion elicitation during responding while maintaining conversational
goals including coherence.
