Geodesic
Word-based russian text augmentation for character-level models
Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part I, Tome 499 (2021), pp. 206-221

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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. 
@article{ZNSL_2021_499_a10,
     author = {R. B. Galinsky and A. M. Alekseev and S. I. Nikolenko},
     title = {Word-based russian text augmentation for character-level models},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {206--221},
     publisher = {mathdoc},
     volume = {499},
     year = {2021},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_2021_499_a10/}
}
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R. B. Galinsky; A. M. Alekseev; S. I. Nikolenko. Word-based russian text augmentation for character-level models. Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part I, Tome 499 (2021), pp. 206-221. http://geodesic.mathdoc.fr/item/ZNSL_2021_499_a10/