VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament

Abstract

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.

Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

“…In this paper, we approach the automatic prediction of the winning stance in complete natural language professional debates. For that purpose, we use the VivesDebate 1 corpus (Ruiz-Dolz et al, 2021b) to conduct all the experiments and the validation of our proposed method. This corpus contains the annotations of the complete lines of reasoning presented by the debaters in a debate tournament, inspired by the AIF (Chesnevar et al, 2006) standard, and the professional jury evaluations of the quality of argumentation presented in each debate.…”

“…Additionally, each debate has the scores of the jury that indicate which team has proposed a more solid and stronger argumentative reasoning. An in-depth analysis of the corpus structure and statistics can be found in (Ruiz-Dolz et al, 2021b).…”

Dynamic Stance: Modeling Discussions by Labeling the Interactions

“…The goal of this approach is to be able to capture the relation of a text with respect to a disputed claim as well as any other dialogic interaction in online forums or micro-blogging platforms. This will be relevant for tasks such as fact verification, but also for controversy detection (Hessel and Lee, 2019;Figueras et al, 2023), argument mining (Lawrence and Reed, 2020;Dutta et al, 2022;Ruiz-Dolz et al, 2022), detecting previously verified claims Shaar et al, 2022;Nakov et al, 2021), and discourse analysis.…”

VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

“…In this work, we present VivesDebate-Speech, an argumentative corpus created to leverage audio features for argument mining tasks. The VivesDebate-Speech has been created taking the previously annotated VivesDebate corpus (Ruiz-Dolz et al, 2021b) as a starting point.…”