  Emotional support is a crucial ability for many conversation scenarios,
including social interactions, mental health support, and customer service
chats. Following reasonable procedures and using various support skills can
help to effectively provide support. However, due to the lack of a
well-designed task and corpora of effective emotional support conversations,
research on building emotional support into dialog systems remains untouched.
In this paper, we define the Emotional Support Conversation (ESC) task and
propose an ESC Framework, which is grounded on the Helping Skills Theory. We
construct an Emotion Support Conversation dataset (ESConv) with rich annotation
(especially support strategy) in a help-seeker and supporter mode. To ensure a
corpus of high-quality conversations that provide examples of effective
emotional support, we take extensive effort to design training tutorials for
supporters and several mechanisms for quality control during data collection.
Finally, we evaluate state-of-the-art dialog models with respect to the ability
to provide emotional support. Our results show the importance of support
strategies in providing effective emotional support and the utility of ESConv
in training more emotional support systems.
