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NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes (2402.14458v1)

Published 22 Feb 2024 in cs.CL and cs.AI

Abstract: Some of the major limitations identified in the areas of argument mining, argument generation, and natural language argument analysis are related to the complexity of annotating argumentatively rich data, the limited size of these corpora, and the constraints that represent the different languages and domains in which these data is annotated. To address these limitations, in this paper we present the following contributions: (i) an effective methodology for the automatic generation of natural language arguments in different topics and languages, (ii) the largest publicly available corpus of natural language argumentation schemes, and (iii) a set of solid baselines and fine-tuned models for the automatic identification of argumentation schemes.

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Authors (4)
  1. Ramon Ruiz-Dolz (7 papers)
  2. Joaquin Taverner (4 papers)
  3. John Lawrence (4 papers)
  4. Chris Reed (8 papers)
Citations (6)
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