Extending Multilingual BERT to Low-Resource Languages

Zihan Wang, Karthikeyan K, Stephen Mayhew, Dan Roth


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
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2649–2656
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.240
DOI:
10.18653/v1/2020.findings-emnlp.240
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.240.pdf