Supplemental Files for the article "Backtranslation Effects on Static and Contextual Word Embeddings for Topic Classification"

This supplementary files were used in a study investigating the impact of backtranslation on topic classification, focusing on two types of word embeddings: static word vectors (FastText) and contextual word embeddings (RoBERTa). The primary aim of the study was to evaluate whether backtranslation enhances classification performance for both embedding types across multiple languages, including Slovak. The dataset includes both original and backtranslated data, which were utilized to train various classifiers such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and RNN-LSTM.