Abstract
Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success is focused only on the top 104 languages in Wikipedia it was trained on. In this paper, we propose a simple but effective approach to extend M-BERT E-MBERT so it can benefit any new language, and show that our approach aids languages that are already in M-BERT as well. We perform an extensive set of experiments with Named Entity Recognition (NER) on 27 languages, only 16 of which are in M-BERT, and show an average increase of about 6% F1 on M-BERT languages and 23% F1 increase on new languages. We release models and code at http://cogcomp.org/page/publication_view/912.- Anthology ID:
- 2020.findings-emnlp.240
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2649–2656
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.findings-emnlp.240/
- DOI:
- 10.18653/v1/2020.findings-emnlp.240
- Cite (ACL):
- Zihan Wang, Karthikeyan K, Stephen Mayhew, and Dan Roth. 2020. Extending Multilingual BERT to Low-Resource Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2649–2656, Online. Association for Computational Linguistics.
- Cite (Informal):
- Extending Multilingual BERT to Low-Resource Languages (Wang et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.findings-emnlp.240.pdf