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Large pretrained masked language models have become state-of-the-artsolutions for many NLP problems. While studies have shown that monolingualmodels produce better results than multilingual models, the training datasetsmust be sufficiently large. We trained a trilingual LitLat BERT-like model forLithuanian, Latvian, and English, and a monolingual Est-RoBERTa model forEstonian. We evaluate their performance on four downstream tasks: named entityrecognition, dependency parsing, part-of-speech tagging, and word analogy. Toanalyze the importance of focusing on a single language and the importance of alarge training set, we compare created models with existing monolingual andmultilingual BERT models for Estonian, Latvian, and Lithuanian. The resultsshow that the newly created LitLat BERT and Est-RoBERTa models improve theresults of existing models on all tested tasks in most situations.

Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages