Improving Large-scale Language Models and Resources for Filipino

• URL: http://arxiv.org/abs/2111.06053v1
• Date: Thu, 11 Nov 2021 05:00:58 GMT
• Title: Improving Large-scale Language Models and Resources for Filipino
• Authors: Jan Christian Blaise Cruz and Charibeth Cheng
• Abstract summary: We outline the construction of the TLUnified dataset, a large-scale pretraining corpus for the Filipino language. Second, we pretrain new Transformer language models following the RoBERTa pretraining technique to supplant existing models trained with small corpora. Our new RoBERTa models show significant improvements over existing Filipino models in three benchmark datasets with an average gain of 4.47% test accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this paper, we improve on existing language resources for the low-resource Filipino language in two ways. First, we outline the construction of the TLUnified dataset, a large-scale pretraining corpus that serves as an improvement over smaller existing pretraining datasets for the language in terms of scale and topic variety. Second, we pretrain new Transformer language models following the RoBERTa pretraining technique to supplant existing models trained with small corpora. Our new RoBERTa models show significant improvements over existing Filipino models in three benchmark datasets with an average gain of 4.47% test accuracy across the three classification tasks of varying difficulty.

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