scholarly journals Applications of Computational Argumentation

Author(s):  
Hajime SAWAMURA
2017 ◽  
Author(s):  
Ivan Habernal ◽  
Raffael Hannemann ◽  
Christian Pollak ◽  
Christopher Klamm ◽  
Patrick Pauli ◽  
...  

2021 ◽  
pp. 93-113
Author(s):  
Oana Cocarascu ◽  
Kristijonas Cyras ◽  
Antonio Rago ◽  
Francesca Toni

Adoption of AI-equipped systems and their societal benefits are heavily dependent on human understanding of the rationale behind the systems’ outputs. Such systems’ widespread inability to explain their outputs causes human mistrust and doubts regarding their regulatory compliance. Research in psychology points to the amenability of argumentation as a paradigm for human reasoning, advocating that humans developed reasoning in order to argue. We here overview a number of approaches using computational argumentation frameworks as the scaffolding for explanations for human consumption. Our argumentation frameworks are automatically mined from data and data-centric methods. We define explanations as graphs obtained from these argumentation frameworks, which are customisable by means of properties. We illustrate our methods with various consumer-oriented tasks in the media and entertainment industry, providing reasoning outputs that can be explained to consumers, and that consumers can directly interact with to give rise to improved recommendations.


2009 ◽  
Vol 24 (5) ◽  
pp. 42-52 ◽  
Author(s):  
Dan Cartwright ◽  
Katie Atkinson

2018 ◽  
Vol 23 (1-2) ◽  
pp. 90-99 ◽  
Author(s):  
Floris J Bex ◽  
Douglas N Walton

We present a computational argumentation approach that models legal reasoning with evidence and proof as dialectical rather than probabilistic. This hybrid approach of stories and arguments models the process of proof in a way that is compatible with Allen and Pardo's theory of relative plausibility by adding arguments that can be used to show how evidence can support or attack explanations. Using some legal cases as examples, we show how criteria for assessing explanations connect arguments and evidence to story schemes. We show how this hybrid dialectical approach avoids the main problem of the probabilistic approaches, namely that they require precise numbers to be applied in order to decide legal cases. We provide an alternative method that allows fact-finders to reason with evidence holistically and not in the item-by-item fashion proposed by the probabilistic account.


2021 ◽  
Vol 11 (15) ◽  
pp. 7160
Author(s):  
Ramon Ruiz-Dolz ◽  
Montserrat Nofre ◽  
Mariona Taulé ◽  
Stella Heras ◽  
Ana García-Fornes

The application of the latest Natural Language Processing breakthroughs in computational argumentation has shown promising results, which have raised the interest in this area of research. However, the available corpora with argumentative annotations are often limited to a very specific purpose or are not of adequate size to take advantage of state-of-the-art deep learning techniques (e.g., deep neural networks). In this paper, we present VivesDebate, a large, richly annotated and versatile professional debate corpus for computational argumentation research. The corpus has been created from 29 transcripts of a debate tournament in Catalan and has been machine-translated into Spanish and English. The annotation contains argumentative propositions, argumentative relations, debate interactions and professional evaluations of the arguments and argumentation. The presented corpus can be useful for research on a heterogeneous set of computational argumentation underlying tasks such as Argument Mining, Argument Analysis, Argument Evaluation or Argument Generation, among others. All this makes VivesDebate a valuable resource for computational argumentation research within the context of massive corpora aimed at Natural Language Processing tasks.


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