  Argumentation mining is a field of computational linguistics that is devoted
to extracting from texts and classifying arguments and relations between them,
as well as constructing an argumentative structure. A significant obstacle to
research in this area for the Russian language is the lack of annotated
Russian-language text corpora. This article explores the possibility of
improving the quality of argumentation mining using the extension of the
Russian-language version of the Argumentative Microtext Corpus (ArgMicro) based
on the machine translation of the Persuasive Essays Corpus (PersEssays). To
make it possible to use these two corpora combined, we propose a Joint Argument
Annotation Scheme based on the schemes used in ArgMicro and PersEssays. We
solve the problem of classifying argumentative discourse units (ADUs) into two
classes - "pro" ("for") and "opp" ("against") using traditional machine
learning techniques (SVM, Bagging and XGBoost) and a deep neural network (BERT
model). An ensemble of XGBoost and BERT models was proposed, which showed the
highest performance of ADUs classification for both corpora.
