Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee

• URL: http://arxiv.org/abs/2101.11870v1
• Date: Thu, 28 Jan 2021 08:49:24 GMT
• Title: Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee
• Authors: Emmanuel Hadoux and Anthony Hunter and Sylwia Polberg
• Abstract summary: Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. We present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues.
• Score: 6.091096843566857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants showing that our automated persuasion system based on this technology is superior to a baseline system that does not take the beliefs and concerns into account in its strategy.

Related papers

• AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments [0.5242869847419834]
  We introduce AutoPersuade, a framework for constructing persuasive messages. We develop a novel topic model to identify argument features that influence persuasiveness. We use this model to predict the effectiveness of new arguments and assess the causal impact of different components.
  arXiv  Detail & Related papers  (2024-10-11T15:46:05Z)
• Conceptual and Unbiased Reasoning in Language Models [98.90677711523645]
  We propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions. We show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making.
  arXiv  Detail & Related papers  (2024-03-30T00:53:53Z)
• The Persuasive Power of Large Language Models [0.0]
  We design a synthetic persuasion dialogue scenario on the topic of climate change. A 'convincer' agent generates a persuasive argument for a'skeptic' agent, who subsequently assesses whether the argument changed its internal opinion state. We ask human judges to evaluate the persuasiveness of machine-generated arguments. Our findings suggest that artificial agents have the potential of playing an important role in collective processes of opinion formation in online social media.
  arXiv  Detail & Related papers  (2023-12-24T16:21:11Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
  We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
  arXiv  Detail & Related papers  (2023-10-18T20:18:05Z)
• Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion [52.49981085431061]
  Enhancing people's ability to write persuasive arguments could contribute to the effectiveness and civility in online communication. We derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions. Persua is an interactive visual system that provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments.
  arXiv  Detail & Related papers  (2022-04-16T08:07:53Z)
• Towards Understanding Persuasion in Computational Argumentation [10.089382889894246]
  Opinion formation and persuasion in argumentation are affected by three major factors: the argument itself, the source of the argument, and the properties of the audience. This thesis makes several contributions to understand the relative effect of the source, audience, and language in computational persuasion.
  arXiv  Detail & Related papers  (2021-10-03T19:36:21Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)
• What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process [78.4766663287415]
  This paper presents a study that automatically analyzes the key factors in argument persuasiveness. We propose a novel neural model that is able to track the changes of latent topics and discourse in argumentative conversations.
  arXiv  Detail & Related papers  (2020-02-10T04:27:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.