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 \textsc{PersuasionForGood} confirm that the proposed model CFO-Net is effective to improve the performance on M-F1 from 61.74 to 65.41.
翻译:战略识别任务要求系统根据对话情况承认说服者采取的战略,然而,以往的方法主要侧重于背景信息,对将心理反馈(即被说服者的情感)纳入预测战略知之甚少,在本文件中,我们提议建立一个跨渠道反馈网(CFO-Net),以利用情感反馈来迭代衡量战略的潜在效益,并将其纳入背景意识对话信息。具体地说,CFO-Net设计了一个反馈记忆模块,包括战略集合和反馈库,以获得感知战略代表。战略集合旨在存储历史战略和反馈库,目的是根据反馈情感信息获得最新的战略权重。此外,还开发了一个跨渠道聚合预测器,以便在情感认知战略代表与背景对话信息之间进行互动,以确认战略。关于\ textsc{PersusausionFourGood}的实验结果证实,拟议的CFO-Net模式对于改进M-F1从6174到6541的性能十分有效。