  The majority of existing methods for empathetic response generation rely on
the emotion of the context to generate empathetic responses. However, empathy
is much more than generating responses with an appropriate emotion. It also
often entails subtle expressions of understanding and personal resonance with
the situation of the other interlocutor. Unfortunately, such qualities are
difficult to quantify and the datasets lack the relevant annotations. To
address this issue, in this paper we propose an approach that relies on
exemplars to cue the generative model on fine stylistic properties that signal
empathy to the interlocutor. To this end, we employ dense passage retrieval to
extract relevant exemplary responses from the training set. Three elements of
human communication -- emotional presence, interpretation, and exploration, and
sentiment are additionally introduced using synthetic labels to guide the
generation towards empathy. The human evaluation is also extended by these
elements of human communication. We empirically show that these approaches
yield significant improvements in empathetic response quality in terms of both
automated and human-evaluated metrics. The implementation is available at
https://github.com/declare-lab/exemplary-empathy.
