Conclusion-based Counter-Argument Generation

• URL: http://arxiv.org/abs/2301.09911v1
• Date: Tue, 24 Jan 2023 10:49:01 GMT
• Title: Conclusion-based Counter-Argument Generation
• Authors: Milad Alshomary and Henning Wachsmuth
• Abstract summary: In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. We propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument.
• Score: 26.540485804067536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. In this paper, we hypothesize that the key to effective counter-argument generation is to explicitly model the argument's conclusion and to ensure that the stance of the generated counter is opposite to that conclusion. In particular, we propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. The approach employs a stance-based ranking component that selects the counter from a diverse set of generated candidates whose stance best opposes the generated conclusion. In both automatic and manual evaluation, we provide evidence that our approach generates more relevant and stance-adhering counters than strong baselines.

Related papers

• Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation [62.069374456021016]
  We present the ArgTersely benchmark for sentence-level counter-argument generation. We also propose Arg-LlaMA for generating high-quality counter-argument.
  arXiv  Detail & Related papers  (2023-12-21T06:51:34Z)
• Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals [70.22179850619519]
  In many domains of argumentation, people's arguments are driven by so-called attitude roots. Recent work in psychology suggests that instead of directly countering surface-level reasoning, one should follow an argumentation style inspired by the Jiu-Jitsu'soft' combat system. We are the first to explore Jiu-Jitsu argumentation for peer review by proposing the novel task of attitude and theme-guided rebuttal generation.
  arXiv  Detail & Related papers  (2023-11-07T13:54:01Z)
• 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)
• ArgU: A Controllable Factual Argument Generator [0.0]
  ArgU is a neural argument generator capable of producing factual arguments from input facts and real-world concepts. We have compiled and released an annotated corpora of 69,428 arguments spanning six topics and six argument schemes.
  arXiv  Detail & Related papers  (2023-05-09T10:49:45Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• 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)
• Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks [2.4861619769660637]
  We propose an original hybrid method to automatically evaluate argumentative debates. For that purpose, we combine concepts from argumentation theory with Transformer-based architectures and neural graph networks. We obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.
  arXiv  Detail & Related papers  (2022-03-28T11:09:07Z)
• Argument Undermining: Counter-Argument Generation by Attacking Weak Premises [31.463885580010192]
  We explore argument undermining, that is, countering an argument by attacking one of its premises. We propose a pipeline approach that first assesses the premises' strength and then generates a counter-argument targeting the weak ones.
  arXiv  Detail & Related papers  (2021-05-25T08:39:14Z)
• Aspect-Controlled Neural Argument Generation [65.91772010586605]
  We train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect. Our evaluation shows that our generation model is able to generate high-quality, aspect-specific arguments. These arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments.
  arXiv  Detail & Related papers  (2020-04-30T20:17:22Z)
• AMPERSAND: Argument Mining for PERSuAsive oNline Discussions [41.06165177604387]
  We propose a computational model for argument mining in online persuasive discussion forums. Our approach relies on identifying relations between components of arguments in a discussion thread. Our models obtain significant improvements compared to recent state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-04-30T10:33:40Z)
• Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model [8.8896707993459]
  The same side stance classification task provides a dataset of argument pairs classified by whether or not both arguments share the same stance. We fine-tuned a pre-trained BERT model for three epochs and used the first 512 tokens of each argument to predict if two arguments share the same stance.
  arXiv  Detail & Related papers  (2020-04-23T13:54:31Z)

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.