Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs

• URL: http://arxiv.org/abs/2001.03436v1
• Date: Thu, 9 Jan 2020 15:19:45 GMT
• Title: Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs
• Authors: Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
• Abstract summary: We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents. Our method allows for interactive reasoning on knowledge graphs where the users can raise additional arguments or evaluate the debate taking common sense reasoning and external information into account.
• Score: 27.225048123690243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, referred to as the judge, decides whether the fact is true or false. The two agents can be considered as sparse feature extractors that present interpretable evidence for either the thesis or the antithesis. In contrast to black-box methods, the arguments enable the user to gain an understanding for the decision of the judge. Moreover, our method allows for interactive reasoning on knowledge graphs where the users can raise additional arguments or evaluate the debate taking common sense reasoning and external information into account. Such interactive systems can increase the acceptance of various AI applications based on knowledge graphs and can further lead to higher efficiency, robustness, and fairness.

Related papers

• 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)
• DebateKG: Automatic Policy Debate Case Creation with Semantic Knowledge Graphs [0.0]
  We show that effective debate cases can be constructed using constrained shortest path traversals on Argumentative Semantic Knowledge Graphs. We significantly improve upon DebateSum by introducing 53180 new examples. We create a unique method for evaluating which knowledge graphs are better in the context of producing policy debate cases.
  arXiv  Detail & Related papers  (2023-07-09T04:19:19Z)
• How to disagree well: Investigating the dispute tactics used on Wikipedia [17.354674873244335]
  We propose a framework of dispute tactics that unifies the perspectives of detecting toxicity and analysing argument structure. This framework includes a preferential ordering among rebuttal-type tactics, ranging from ad hominem attacks to refuting the central argument. We show that these annotations can be used to provide useful additional signals to improve performance on the task of predicting escalation.
  arXiv  Detail & Related papers  (2022-12-16T09:01:19Z)
• Explaining Image Classification with Visual Debates [26.76139301708958]
  We propose a novel debate framework for understanding and explaining a continuous image classifier's reasoning for making a particular prediction. Our framework encourages players to put forward diverse arguments during the debates, picking up the reasoning trails missed by their opponents. We demonstrate and evaluate (a practical realization) our Visual Debates on the geometric SHAPE and MNIST datasets.
  arXiv  Detail & Related papers  (2022-10-17T12:35:52Z)
• 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 Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems [109.16553492049441]
  We propose a novel method to incorporate the knowledge reasoning capability into dialogue systems in a more scalable and generalizable manner. To the best of our knowledge, this is the first work to have transformer models generate responses by reasoning over differentiable knowledge graphs.
  arXiv  Detail & Related papers  (2022-03-20T17:51:49Z)
• Annotating Implicit Reasoning in Arguments with Causal Links [34.77514899468729]
  We focus on identifying the implicit knowledge in the form of argumentation knowledge. Being inspired by the Argument from Consequences scheme, we propose a semi-structured template to represent such argumentation knowledge. We show how to collect and filter high-quality implicit reasonings via crowdsourcing.
  arXiv  Detail & Related papers  (2021-10-26T13:28:53Z)
• Prompting Contrastive Explanations for Commonsense Reasoning Tasks [74.7346558082693]
  Large pretrained language models (PLMs) can achieve near-human performance on commonsense reasoning tasks. We show how to use these same models to generate human-interpretable evidence.
  arXiv  Detail & Related papers  (2021-06-12T17:06:13Z)
• Contrastive Explanations for Model Interpretability [77.92370750072831]
  We propose a methodology to produce contrastive explanations for classification models. Our method is based on projecting model representation to a latent space. Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
  arXiv  Detail & Related papers  (2021-03-02T00:36:45Z)
• KompaRe: A Knowledge Graph Comparative Reasoning System [85.72488258453926]
  This paper introduces comparative reasoning over knowledge graphs, which aims to infer the commonality and inconsistency with respect to multiple pieces of clues. We develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
  arXiv  Detail & Related papers  (2020-11-06T04:57:37Z)
• Reasoning on Knowledge Graphs with Debate Dynamics [27.225048123690243]
  We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents. We benchmark our method on the triple classification and link prediction task.
  arXiv  Detail & Related papers  (2020-01-02T14:44:23Z)

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.