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

FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation