KinyaBERT: a Morphology-aware Kinyarwanda Language Model

• URL: http://arxiv.org/abs/2203.08459v2
• Date: Thu, 17 Mar 2022 12:35:21 GMT
• Title: KinyaBERT: a Morphology-aware Kinyarwanda Language Model
• Authors: Antoine Nzeyimana, Andre Niyongabo Rubungo
• Abstract summary: Unsupervised sub-word tokenization methods are sub-optimal at handling morphologically rich languages. We propose a simple yet effective two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphological compositionality. We evaluate our proposed method on the low-resource morphologically rich Kinyarwanda language, naming the proposed model architecture KinyaBERT.
• Score: 1.2183405753834562
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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