  Researches on dialogue empathy aim to endow an agent with the capacity of
accurate understanding and proper responding for emotions. Existing models for
empathetic dialogue generation focus on the emotion flow in one direction, that
is, from the context to response. We argue that conducting an empathetic
conversation is a bidirectional process, where empathy occurs when the emotions
of two interlocutors could converge on the same point, i.e., reaching an
emotion consensus. Besides, we also find that the empathetic dialogue corpus is
extremely limited, which further restricts the model performance. To address
the above issues, we propose a dual-generative model, Dual-Emp, to
simultaneously construct the emotion consensus and utilize some external
unpaired data. Specifically, our model integrates a forward dialogue model, a
backward dialogue model, and a discrete latent variable representing the
emotion consensus into a unified architecture. Then, to alleviate the
constraint of paired data, we extract unpaired emotional data from open-domain
conversations and employ Dual-Emp to produce pseudo paired empathetic samples,
which is more efficient and low-cost than the human annotation. Automatic and
human evaluations demonstrate that our method outperforms competitive baselines
in producing coherent and empathetic responses.
