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)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.581.pdf
- Data
- MOROCO