Challenge: Persuasive dialog systems have various usages, such as donation persuation and physical exercise persulasion.
Approach: They adopt a preliminary framework on persuasion resistance in psychology and build a fine-grained resistance strategy annotation scheme to analyze the persuitee's resistance strategies.
Outcome: The proposed system can understand and address user resistance strategies appropriately.

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ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations (2021.eacl-main)

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Challenge: Existing research has failed to account for resisting strategies employed to foil persuasion attempts.
Approach: They propose a framework for identifying resisting strategies in persuasive conversations . they instantiate a dataset comprising persuasion and negotiation conversations based on a hierarchical sequence-labelling neural architecture .
Outcome: The proposed framework is based on two persuasive conversation datasets and leverages a hierarchical sequence-labelling neural architecture to infer resisting strategies automatically.
Detecting Winning Arguments with Large Language Models and Persuasion Strategies (2026.findings-eacl)

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Challenge: Recent studies have focused on predicting winning arguments, i.e., those that effectively convince a reader to adopt a certain opinion.
Approach: They propose to use large language models with a chain-of-thought framework to guide reasoning over six persuasion strategies to determine persuasiveness.
Outcome: The proposed approach leverages large language models with a chain-of-thought framework that guides reasoning over six persuasion strategies.
Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)

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Challenge: Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation.
Approach: They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor.
Outcome: The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets.
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good (P19-1)

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Challenge: Persuasion agents are a form of communication that can be used to change people's opinions and actions for social good.
Approach: They designed an online persuasion task where one participant was asked to persult the other to donate to a specific charity.
Outcome: The proposed system could change people's opinions and actions for social good.
Using Persuasive Writing Strategies to Explain and Detect Health Misinformation (2024.lrec-main)

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Challenge: Increasing misinformation has led to a decrease in trust in news organizations and a decline in the health and medical industry.
Approach: They propose a novel annotation scheme that incorporates persuasive writing tactics in textual documents to aid the automatic identification of misinformation.
Outcome: The proposed scheme improves accuracy and explainability of misinformation detection models.
Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms (N19-1)

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Challenge: Existing models can't quantify persuasiveness of requests or extract successful persuasive strategies.
Approach: They propose a semi-supervised hierarchical neural network model to quantify persuasiveness and identify persuasive strategies in advocacy requests.
Outcome: The proposed method outperforms baseline models and offers increased interpretability of persuasive speech.
You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations (2026.findings-acl)

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Challenge: Psychological defenses are strategies people use to manage distress.
Approach: They propose a dialogue corpus with help seeker utterances labeled for defense level and a DMRS Co-Pilot pipeline that provides evidence-based pre-annotations.
Outcome: The proposed framework reduces annotation time by 24.0% in a counterbalanced study.
AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments (2024.emnlp-main)

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Challenge: Existing tools for persuasion are well-equipped to identify which of a pre-existing set of messages is most persuasive, but they do not offer causal evidence on whether or how they have succeeded.
Approach: They propose a framework for identifying topical components of persuasive arguments that are autopersuade.
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Would You Like to Make a Donation? A Dialogue System to Persuade You to Donate (2024.lrec-main)

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Challenge: Persuasive automated dialogue systems are a popular way to influence people's behavior and decision making.
Approach: They propose to use a context-aware persuasion strategy selection module to persult users . they also propose a persuasiveness prediction model to automatically evaluate the persuasiveness of generated text.
Outcome: The proposed system can achieve better performance on several automated evaluation metrics than baseline models.
Enhancing Persuasive Dialogue Agents by Synthesizing Cross‐Disciplinary Communication Strategies (2025.emnlp-industry)

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Challenge: Current approaches to developing persuasive dialogue agents rely on predefined persuasive strategies that fail to capture the complexity of real-world interactions.
Approach: They propose a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory.
Outcome: The proposed framework demonstrated significant improvement in the persuasion success rate and generalizability of the datasets.

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