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In this paper, we improve on existing language resources for the low-resourceFilipino language in two ways. First, we outline the construction of theTLUnified dataset, a large-scale pretraining corpus that serves as animprovement over smaller existing pretraining datasets for the language interms of scale and topic variety. Second, we pretrain new Transformer languagemodels following the RoBERTa pretraining technique to supplant existing modelstrained with small corpora. Our new RoBERTa models show significantimprovements over existing Filipino models in three benchmark datasets with anaverage gain of 4.47% test accuracy across the three classification tasks ofvarying difficulty.

Improving Large-scale Language Models and Resources for Filipino