Word-based russian text augmentation for character-level models

Large-scale deep learning models, including models for natural language processing, require large datasets for training that could be unavailable for low-resource languages or for special domains. We consider a way to approach the problem of poor variability and small size of available data for training NLP models based on augmenting the data with synonyms. We design a novel augmentation scheme that includes replacing words with synonyms and reshuffling the words, apply it to the Russian language, and report improved results for the sentiment analysis task.