RoBERT – A Romanian BERT Model

Mihai Masala, Stefan Ruseti, Mihai Dascalu


Abstract
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 multi-lingual 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.
Anthology ID:
2020.coling-main.581
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6626–6637
Language:
URL:
https://aclanthology.org/2020.coling-main.581
DOI:
10.18653/v1/2020.coling-main.581
Bibkey:
Cite (ACL):
Mihai Masala, Stefan Ruseti, and Mihai Dascalu. 2020. RoBERT – A Romanian BERT Model. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6626–6637, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
RoBERT – A Romanian BERT Model (Masala et al., COLING 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.581.pdf
Data
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