  Emotion-controllable response generation is an attractive and valuable task
that aims to make open-domain conversations more empathetic and engaging.
Existing methods mainly enhance the emotion expression by adding regularization
terms to standard cross-entropy loss and thus influence the training process.
However, due to the lack of further consideration of content consistency, the
common problem of response generation tasks, safe response, is intensified.
Besides, query emotions that can help model the relationship between query and
response are simply ignored in previous models, which would further hurt the
coherence. To alleviate these problems, we propose a novel framework named
Curriculum Dual Learning (CDL) which extends the emotion-controllable response
generation to a dual task to generate emotional responses and emotional queries
alternatively. CDL utilizes two rewards focusing on emotion and content to
improve the duality. Additionally, it applies curriculum learning to gradually
generate high-quality responses based on the difficulties of expressing various
emotions. Experimental results show that CDL significantly outperforms the
baselines in terms of coherence, diversity, and relation to emotion factors.
