Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System

Abstract

Persuasive strategy recognition task requires the system to recognize the adopted strategy of the persuader according to the conversation. However, previous methods mainly focus on the contextual information, little is known about incorporating the psychological feedback, i.e. emotion of the persuadee, to predict the strategy. In this paper, we propose a Cross-channel Feedback memOry Network (CFO-Net) to leverage the emotional feedback to iteratively measure the potential benefits of strategies and incorporate them into the contextual-aware dialogue information. Specifically, CFO-Net designs a feedback memory module, including strategy pool and feedback pool, to obtain emotion-aware strategy representation. The strategy pool aims to store historical strategies and the feedback pool is to obtain updated strategy weight based on feedback emotional information. Furthermore, a cross-channel fusion predictor is developed to make a mutual interaction between the emotion-aware strategy representation and the contextual-aware dialogue information for strategy recognition. Experimental results on PersuasionForGood confirm that the proposed model CFO-Net is effective to improve the performance on M-F1 from 61.74 to 65.41.

GE-Blender: Graph-Based Knowledge Enhancement for Blender

“…Unseen entity problem leads to the failure of the model in recognizing semantics in open-domain dialogue generation [3,[18][19][20]29] and some previous methods improved on this issue. and some previous methods have made many improvements to this issue.…”