@inproceedings{masala-etal-2021-jurbert,
title = "jur{BERT}: A {R}omanian {BERT} Model for Legal Judgement Prediction",
author = "Masala, Mihai and
Iacob, Radu Cristian Alexandru and
Uban, Ana Sabina and
Cidota, Marina and
Velicu, Horia and
Rebedea, Traian and
Popescu, Marius",
editor = "Aletras, Nikolaos and
Androutsopoulos, Ion and
Barrett, Leslie and
Goanta, Catalina and
Preotiuc-Pietro, Daniel",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nllp-1.8",
doi = "10.18653/v1/2021.nllp-1.8",
pages = "86--94",
abstract = "Transformer-based models have become the de facto standard in the field of Natural Language Processing (NLP). By leveraging large unlabeled text corpora, they enable efficient transfer learning leading to state-of-the-art results on numerous NLP tasks. Nevertheless, for low resource languages and highly specialized tasks, transformer models tend to lag behind more classical approaches (e.g. SVM, LSTM) due to the lack of aforementioned corpora. In this paper we focus on the legal domain and we introduce a Romanian BERT model pre-trained on a large specialized corpus. Our model outperforms several strong baselines for legal judgement prediction on two different corpora consisting of cases from trials involving banks in Romania.",
}
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%0 Conference Proceedings
%T jurBERT: A Romanian BERT Model for Legal Judgement Prediction
%A Masala, Mihai
%A Iacob, Radu Cristian Alexandru
%A Uban, Ana Sabina
%A Cidota, Marina
%A Velicu, Horia
%A Rebedea, Traian
%A Popescu, Marius
%Y Aletras, Nikolaos
%Y Androutsopoulos, Ion
%Y Barrett, Leslie
%Y Goanta, Catalina
%Y Preotiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F masala-etal-2021-jurbert
%X Transformer-based models have become the de facto standard in the field of Natural Language Processing (NLP). By leveraging large unlabeled text corpora, they enable efficient transfer learning leading to state-of-the-art results on numerous NLP tasks. Nevertheless, for low resource languages and highly specialized tasks, transformer models tend to lag behind more classical approaches (e.g. SVM, LSTM) due to the lack of aforementioned corpora. In this paper we focus on the legal domain and we introduce a Romanian BERT model pre-trained on a large specialized corpus. Our model outperforms several strong baselines for legal judgement prediction on two different corpora consisting of cases from trials involving banks in Romania.
%R 10.18653/v1/2021.nllp-1.8
%U https://aclanthology.org/2021.nllp-1.8
%U https://doi.org/10.18653/v1/2021.nllp-1.8
%P 86-94
Markdown (Informal)
[jurBERT: A Romanian BERT Model for Legal Judgement Prediction](https://aclanthology.org/2021.nllp-1.8) (Masala et al., NLLP 2021)
ACL
- Mihai Masala, Radu Cristian Alexandru Iacob, Ana Sabina Uban, Marina Cidota, Horia Velicu, Traian Rebedea, and Marius Popescu. 2021. jurBERT: A Romanian BERT Model for Legal Judgement Prediction. In Proceedings of the Natural Legal Language Processing Workshop 2021, pages 86–94, Punta Cana, Dominican Republic. Association for Computational Linguistics.