  Empathy is a crucial factor in open-domain conversations, which naturally
shows one's caring and understanding to others. Though several methods have
been proposed to generate empathetic responses, existing works often lead to
monotonous empathy that refers to generic and safe expressions. In this paper,
we propose to use explicit control to guide the empathy expression and design a
framework DiffusEmp based on conditional diffusion language model to unify the
utilization of dialogue context and attribute-oriented control signals.
Specifically, communication mechanism, intent, and semantic frame are imported
as multi-grained signals that control the empathy realization from coarse to
fine levels. We then design a specific masking strategy to reflect the
relationship between multi-grained signals and response tokens, and integrate
it into the diffusion model to influence the generative process. Experimental
results on a benchmark dataset EmpatheticDialogue show that our framework
outperforms competitive baselines in terms of controllability, informativeness,
and diversity without the loss of context-relatedness.
