Knowledge-enhanced Mixed-initiative Dialogue System for Emotional
  Support Conversations
Yang Deng Wenxuan Zhang Yifei Yuan Wai Lam
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.