SERENGETI: Massively Multilingual Language Models for Africa
Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed, Alcides Alcoba Inciarte
Abstract
Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. To date, only ~31 out of ~2,000 African languages are covered in existing language models. We ameliorate this limitation by developing SERENGETI, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to 4 mPLMs that cover 4-23 African languages. SERENGETI outperforms other models on 11 datasets across the eights tasks, achieving 82.27 average F_1. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research. Anonymous link- Anthology ID:
- 2023.findings-acl.97
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1498–1537
- Language:
- URL:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.findings-acl.97/
- DOI:
- 10.18653/v1/2023.findings-acl.97
- Cite (ACL):
- Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed, and Alcides Alcoba Inciarte. 2023. SERENGETI: Massively Multilingual Language Models for Africa. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1498–1537, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- SERENGETI: Massively Multilingual Language Models for Africa (Adebara et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.findings-acl.97.pdf