ArgU: A Controllable Factual Argument Generator

• URL: http://arxiv.org/abs/2305.05334v1
• Date: Tue, 9 May 2023 10:49:45 GMT
• Title: ArgU: A Controllable Factual Argument Generator
• Authors: Sougata Saha and Rohini Srihari
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. For example, persuading someone to reconsider smoking might involve empathetic, well founded arguments based on facts and expert opinions about its ill-effects and the consequences on one's family. However, the automatic generation of high-quality factual arguments can be challenging. Addressing existing controllability issues can make the recent advances in computational models for argument generation a potential solution. In this paper, we introduce ArgU: a neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton's argument scheme-based control codes. Unfortunately, computational argument generation is a relatively new field and lacks datasets conducive to training. Hence, we have compiled and released an annotated corpora of 69,428 arguments spanning six topics and six argument schemes, making it the largest publicly available corpus for identifying argument schemes; the paper details our annotation and dataset creation framework. We further experiment with an argument generation strategy that establishes an inference strategy by generating an ``argument template'' before actual argument generation. Our results demonstrate that it is possible to automatically generate diverse arguments exhibiting different inference patterns for the same set of facts by using control codes based on argument schemes and stance.

Related papers

• "Let's Argue Both Sides": Argument Generation Can Force Small Models to Utilize Previously Inaccessible Reasoning Capabilities [0.8999666725996974]
  We propose Argument Generation as a method of forcing models to utilize their reasoning capabilities. Our method involves the generation of arguments for each possible inference result, and asking the end model to rank the generated arguments.
  arXiv  Detail & Related papers  (2024-10-16T19:49:30Z)
• 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)
• Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition [49.38757847011105]
  computational fallacy recognition faces challenges due to diverse genres, domains, and types of fallacies found in datasets. We aim to enhance existing models for fallacy recognition by incorporating additional context and by leveraging large language models to generate synthetic data. Our evaluation results demonstrate consistent improvements across fallacy types, datasets, and generators.
  arXiv  Detail & Related papers  (2023-11-16T04:17:47Z)
• 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)
• AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining [0.0]
  We propose a novel neural model called AutoAM to solve these problems. Our model is a universal end-to-end framework, which can analyze argument structure without constraints like tree structure.
  arXiv  Detail & Related papers  (2023-09-17T15:26:21Z)
• Retrieval-Augmented Generative Question Answering for Event Argument Extraction [66.24622127143044]
  We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction. It retrieves the most similar QA pair and augments it as prompt to the current example's context, then decodes the arguments as answers. Our approach outperforms substantially prior methods across various settings.
  arXiv  Detail & Related papers  (2022-11-14T02:00:32Z)
• Explainable AI through the Learning of Arguments [0.0]
  Symbolic machine learning techniques learn a set of arguments as an intermediate representation. We investigate the learning of arguments from a 'case model' proposed by Verheij. We compare the learning of arguments from a case model with the HeRO algorithm and learning a decision tree.
  arXiv  Detail & Related papers  (2022-02-01T12:52:30Z)
• 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)
• 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)

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.