  Emotional Support Conversation (ESConv) aims to reduce help-seekers'emotional
distress with the supportive strategy and response. It is essential for the
supporter to select an appropriate strategy with the feedback of the
help-seeker (e.g., emotion change during dialog turns, etc) in ESConv. However,
previous methods mainly focus on the dialog history to select the strategy and
ignore the help-seeker's feedback, leading to the wrong and user-irrelevant
strategy prediction. In addition, these approaches only model the
context-to-strategy flow and pay less attention to the strategy-to-context flow
that can focus on the strategy-related context for generating the
strategy-constrain response. In this paper, we propose a Feedback-Aware Double
COntrolling Network (FADO) to make a strategy schedule and generate the
supportive response. The core module in FADO consists of a dual-level feedback
strategy selector and a double control reader. Specifically, the dual-level
feedback strategy selector leverages the turn-level and conversation-level
feedback to encourage or penalize strategies. The double control reader
constructs the novel strategy-to-context flow for generating the
strategy-constrain response. Furthermore, a strategy dictionary is designed to
enrich the semantic information of the strategy and improve the quality of
strategy-constrain response. Experimental results on ESConv show that the
proposed FADO has achieved the state-of-the-art performance in terms of both
strategy selection and response generation. Our code is available at
https://github.com/Thedatababbler/FADO.
