  Recent years have witnessed great progress on building emotional chatbots.
Tremendous methods have been proposed for chatbots to generate responses with
given emotions. However, the emotion changes of the user during the
conversation has not been fully explored. In this work, we study the problem of
positive emotion elicitation, which aims to generate responses that can elicit
positive emotion of the user, in human-machine conversation. We propose a
weakly supervised Emotion Eliciting Machine (EEM) to address this problem.
Specifically, we first collect weak labels of user emotion status changes in a
conversion based on a pre-trained emotion classifier. Then we propose a dual
encoder-decoder structure to model the generation of responses in both positive
and negative side based on the changes of the user's emotion status in the
conversation. An emotion eliciting factor is introduced on top of the dual
structure to balance the positive and negative emotional impacts on the
generated response during emotion elicitation. The factor also provides a
fine-grained controlling manner for emotion elicitation. Experimental results
on a large real-world dataset show that EEM outperforms the existing models in
generating responses with positive emotion elicitation.
