Improving Neural Network Models for Natural Language Processing in Russian with Synonyms

Recent advances in deep learning for natural language processing achieve and improve over state of the art results in many natural language processing tasks. One problem with neural network models, however, is that they require large datasets, including large labeled datasets for the corresponding problems. In this work, we suggest a data augmentation method based on extending a given dataset with synonyms for the words appearing there. We apply this approach to the morphologically rich Russian language and show improvements for modern neural network NLP models on standard tasks such as sentiment analysis.