What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process

• URL: http://arxiv.org/abs/2002.03536v1
• Date: Mon, 10 Feb 2020 04:27:48 GMT
• Title: What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process
• Authors: Jichuan Zeng, Jing Li, Yulan He, Cuiyun Gao, Michael R. Lyu, Irwin King
• Abstract summary: 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.
• Score: 78.4766663287415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful - topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations.

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)
• 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)
• E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches [57.279044079196105]
  E-ffective is a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information.
  arXiv  Detail & Related papers  (2021-10-28T06:14:27Z)
• 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)
• Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations [70.35379323231241]
  This paper presents a better approach for event extraction by explicitly utilizing the relationships of event arguments. We employ reinforcement learning and incremental learning to extract multiple arguments via a multi-turned, iterative process. Experimental results show that our approach consistently outperforms seven state-of-the-art event extraction methods.
  arXiv  Detail & Related papers  (2021-06-23T13:24:39Z)
• Exploring Discourse Structures for Argument Impact Classification [48.909640432326654]
  This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. We propose DisCOC to inject and fuse the sentence-level structural information with contextualized features derived from large-scale language models.
  arXiv  Detail & Related papers  (2021-06-02T06:49:19Z)
• Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure [53.77234444565652]
  We identify the responding relations in the conversation discourse, which link response utterances to their initiations. We propose a model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts.
  arXiv  Detail & Related papers  (2021-04-17T17:46:00Z)
• Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee [6.091096843566857]
  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.
  arXiv  Detail & Related papers  (2021-01-28T08:49:24Z)
• The Role of Pragmatic and Discourse Context in Determining Argument Impact [39.70446357000737]
  This paper presents a new dataset to initiate the study of this aspect of argumentation. It consists of a diverse collection of arguments covering 741 controversial topics and comprising over 47,000 claims. We propose predictive models that incorporate the pragmatic and discourse context of argumentative claims and show that they outperform models that rely on claim-specific linguistic features for predicting the perceived impact of individual claims within a particular line of argument.
  arXiv  Detail & Related papers  (2020-04-06T23:00:37Z)

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.