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arXiv · 1907.00409

Evaluating Language Model Finetuning Techniques for Low-resource Languages

Jan Christian Blaise Cruz·Charibeth Cheng
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Abstract

Unlike mainstream languages (such as English and French), low-resource languages often suffer from a lack of expert-annotated corpora and benchmark resources that make it hard to apply state-of-the-art techniques directly. In this paper, we alleviate this scarcity problem for the low-resourced Filipino language in two ways. First, we introduce a new benchmark language modeling dataset in Filipino which we call WikiText-TL-39. Second, we show that language model finetuning techniques such as BERT and ULMFiT can be used to consistently train robust classifiers in low-resource settings, experiencing at most a 0.0782 increase in validation error when the number of training examples is decreased from 10K to 1K while finetuning using a privately-held sentiment dataset.

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Jan Christian Blaise Cruz, Charibeth Cheng. 2019-06-30. Evaluating Language Model Finetuning Techniques for Low-resource Languages. https://doi.org/10.13140/rg.2.2.23028.40322

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