  Argumentation analysis is a field of computational linguistics that studies
methods for extracting arguments from texts and the relationships between them,
as well as building argumentation structure of texts. This paper is a report of
the organizers on the first competition of argumentation analysis systems
dealing with Russian language texts within the framework of the Dialogue
conference. During the competition, the participants were offered two tasks:
stance detection and argument classification. A corpus containing 9,550
sentences (comments on social media posts) on three topics related to the
COVID-19 pandemic (vaccination, quarantine, and wearing masks) was prepared,
annotated, and used for training and testing. The system that won the first
place in both tasks used the NLI (Natural Language Inference) variant of the
BERT architecture, automatic translation into English to apply a specialized
BERT model, retrained on Twitter posts discussing COVID-19, as well as
additional masking of target entities. This system showed the following
results: for the stance detection task an F1-score of 0.6968, for the argument
classification task an F1-score of 0.7404. We hope that the prepared dataset
and baselines will help to foster further research on argument mining for the
Russian language.
