  Unlike empathetic dialogues, the system in emotional support conversations
(ESC) is expected to not only convey empathy for comforting the help-seeker,
but also proactively assist in exploring and addressing their problems during
the conversation. In this work, we study the problem of mixed-initiative ESC
where the user and system can both take the initiative in leading the
conversation. Specifically, we conduct a novel analysis on mixed-initiative ESC
systems with a tailor-designed schema that divides utterances into different
types with speaker roles and initiative types. Four emotional support metrics
are proposed to evaluate the mixed-initiative interactions. The analysis
reveals the necessity and challenges of building mixed-initiative ESC systems.
In the light of this, we propose a knowledge-enhanced mixed-initiative
framework (KEMI) for ESC, which retrieves actual case knowledge from a
large-scale mental health knowledge graph for generating mixed-initiative
responses. Experimental results on two ESC datasets show the superiority of
KEMI in both content-preserving evaluation and mixed initiative related
analyses.
