FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation
Wei Peng, Ziyuan Qin, Yue Hu, Yuqiang Xie, Yunpeng Li

TL;DR
FADO introduces a feedback-aware double controlling network that improves strategy selection and response generation in emotional support conversations by leveraging feedback and a novel strategy-to-context flow.
Contribution
The paper proposes FADO, a novel network with dual-level feedback strategy selection and double control reader for better emotional support dialogue modeling.
Findings
Achieves state-of-the-art performance on ESConv dataset.
Effectively utilizes feedback for strategy prediction.
Improves quality of strategy-constrained responses.
Abstract
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…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Topic Modeling · Mental Health via Writing
