RoBERT – A Romanian BERT Model

Deep pre-trained language models tend to become ubiquitous in the field of Natural Language Processing (NLP).These models learn contextualized representations by using a huge amount of unlabeled text data and obtain state of the art results on a multitude of NLP tasks, by enabling efficient transfer learning.For other languages besides English, there are limited options of such models, most of which are trained only on multi-lingual corpora.In this paper we introduce a Romanian-only pre-trained BERT model -RoBERT -and compare it with different multilingual models on seven Romanian specific NLP tasks grouped into three categories, namely: sentiment analysis, dialect and cross-dialect topic identification, and diacritics restoration.Our model surpasses the multi-lingual models, as well as a another mono-lingual implementation of BERT, on all tasks.

@inproceedings{masala2020robert,
  title = {RoBERT – A Romanian BERT Model},
  author = {Mihai Masala and Ştefan Ruşeţi and Mihai Dascălu},
  year = {2020},
  booktitle = {Proceedings of the International Conference on Computational Linguistics (COLING)},
}