Better Quality Pre-training Data and T5 Models for African Languages

Akintunde Oladipo, Mofetoluwa Adeyemi, Orevaoghene Ahia, Abraham Toluwase Owodunni, Odunayo Ogundepo, David Ifeoluwa Adelani, Jimmy Lin

Submission Type: Regular Short Paper

Submission Track: Multilinguality and Linguistic Diversity

Submission Track 2: Resources and Evaluation

Keywords: multilingual, low-resource languages, african languages

TL;DR: We release the largest quality pre-training data across 16 African languages and pretrain better T5 models

Abstract:

In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models.
Existing web crawls have demonstrated quality issues, particularly in the context of low-resource languages.
Consequently, we introduce a new multilingual pretraining corpus for $16$ African languages, designed by carefully auditing existing pretraining corpora to understand and rectify prevalent quality issues. To compile this dataset, we undertake a rigorous examination of current data sources for thirteen languages within one of the most extensive multilingual web crawls, mC4, and extract cleaner data through meticulous auditing and improved web crawling strategies. Subsequently, we pretrain a new T5-based model on this dataset and evaluate its performance on multiple downstream tasks.
Our model demonstrates better downstream effectiveness over existing pretrained models across four NLP tasks, underscoring the critical role data quality plays in pretraining language models in low-resource scenarios. Specifically, on cross-lingual QA evaluation, our new model is more than twice as effective as multilingual T5. All code, data and models are publicly available at https://github.com/castorini/AfriTeVa-keji.

Submission Number: 4823