Abstract
Pre-trained language models such as BERT have been successful at tackling many natural language processing tasks. However, the unsupervised sub-word tokenization methods commonly used in these models (e.g., byte-pair encoding - BPE) are sub-optimal at handling morphologically rich languages. Even given a morphological analyzer, naive sequencing of morphemes into a standard BERT architecture is inefficient at capturing morphological compositionality and expressing word-relative syntactic regularities. We address these challenges by proposing a simple yet effective two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphological compositionality.Despite the success of BERT, most of its evaluations have been conducted on high-resource languages, obscuring its applicability on low-resource languages. We evaluate our proposed method on the low-resource morphologically rich Kinyarwanda language, naming the proposed model architecture KinyaBERT. A robust set of experimental results reveal that KinyaBERT outperforms solid baselines by 2% in F1 score on a named entity recognition task and by 4.3% in average score of a machine-translated GLUE benchmark. KinyaBERT fine-tuning has better convergence and achieves more robust results on multiple tasks even in the presence of translation noise.- Anthology ID:
- 2022.acl-long.367
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5347–5363
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.367
- DOI:
- 10.18653/v1/2022.acl-long.367
- Award:
- Best Linguistic Insight Paper
- Cite (ACL):
- Antoine Nzeyimana and Andre Niyongabo Rubungo. 2022. KinyaBERT: a Morphology-aware Kinyarwanda Language Model. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5347–5363, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- KinyaBERT: a Morphology-aware Kinyarwanda Language Model (Nzeyimana & Niyongabo Rubungo, ACL 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.acl-long.367.pdf
- Code
- anzeyimana/kinyabert-acl2022
- Data
- GLUE, QNLI