Abstract
Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable effective cross-lingual zero-shot transfer. However, their performance on Arabic information extraction (IE) tasks is not very well studied. In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. We study GigaBERT’s effectiveness on zero-short transfer across four IE tasks: named entity recognition, part-of-speech tagging, argument role labeling, and relation extraction. Our best model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT (Antoun et al., 2020) in both the supervised and zero-shot transfer settings. We have made our pre-trained models publicly available at: https://github.com/lanwuwei/GigaBERT.- Anthology ID:
- 2020.emnlp-main.382
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4727–4734
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.382
- DOI:
- 10.18653/v1/2020.emnlp-main.382
- Cite (ACL):
- Wuwei Lan, Yang Chen, Wei Xu, and Alan Ritter. 2020. An Empirical Study of Pre-trained Transformers for Arabic Information Extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4727–4734, Online. Association for Computational Linguistics.
- Cite (Informal):
- An Empirical Study of Pre-trained Transformers for Arabic Information Extraction (Lan et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.emnlp-main.382.pdf
- Code
- lanwuwei/GigaBERT
- Data
- Panlex