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

arXiv: https://arxiv.org/abs/2206.12101 · HTML: https://mlsanity.com/paper/2206.12101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Decoupling Strategy and Generation in Negotiation Dialogues](https://mlsanity.com/paper/1808.09637/) (2018)
- [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings](https://mlsanity.com/paper/1704.07130/) (2017)
- [Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation](https://mlsanity.com/paper/2204.12749/) (2022)
- [Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1804.00775/) (2018)
- [Machine Comprehension Using Match-LSTM and Answer Pointer](https://mlsanity.com/paper/1608.07905/) (2016)
- [RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations](https://mlsanity.com/paper/2101.10545/) (2021)
- [Utterance-level Dialogue Understanding: An Empirical Study](https://mlsanity.com/paper/2009.13902/) (2020)
- [An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text](https://mlsanity.com/paper/1912.06745/) (2019)
- [Emotionally-Aware Chatbots: A Survey](https://mlsanity.com/paper/1906.09774/) (2019)
- [Tartan: A retrieval-based socialbot powered by a dynamic finite-state machine architecture](https://mlsanity.com/paper/1812.01260/) (2018)
- [Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue](https://mlsanity.com/paper/1709.05411/) (2017)
- [Persuasive Dialogue Understanding: the Baselines and Negative Results](https://mlsanity.com/paper/2011.09954/) (2020)

## Built on by (most cited first, 1 of 1)

- [GE-Blender: Graph-Based Knowledge Enhancement for Blender](https://mlsanity.com/paper/2301.12850/) (2023)

## Abstract (arXiv)

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 textscPersuasionForGood confirm that the proposed model CFO-Net is effective to improve the performance on M-F1 from 61.74 to 65.41.