NLAS-multi: A Multilingual Corpus of Automatically Generated Natural
  Language Argumentation Schemes
Ramon Ruiz-Dolz Joaquin Taverner John Lawrence Chris Reed
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.