21 citations · 40 across the 4 of their papers we have counts for
4 papers
Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages
Matej Ulčar, Marko Robnik-Šikonja
Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. While studies have shown that monolingual models produce better results than m…
Evaluation of contextual embeddings on less-resourced languages
Matej Ulčar, Aleš Žagar, Carlos S. Armendariz +4
The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives. Most existing work focuses…
FinEst BERT and CroSloEngual BERT: less is more in multilingual models
Matej Ulčar, Marko Robnik-Šikonja
Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. The research has been mostly focused on English language, though. While massiv…
High Quality ELMo Embeddings for Seven Less-Resourced Languages
Matej Ulčar, Marko Robnik-Šikonja
Recent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task. We offer pre…