Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

TL;DR

This paper investigates argumentation mining in Russian texts by extending existing corpora, proposing a joint annotation scheme, and applying traditional machine learning and deep learning models, including an ensemble, to classify argumentative units.

Contribution

It introduces a joint annotation scheme for Russian argumentation corpora and evaluates ensemble models combining XGBoost and BERT for classifying argumentative discourse units.

Findings

Ensemble of XGBoost and BERT achieved highest classification performance.

Extended Russian argumentation corpus improves data availability.

Deep learning models outperform traditional machine learning methods.

Abstract

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.