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arXiv · 2004.10964

Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Suchin Gururangan·Ana Marasović·Swabha Swayamdipta·Kyle Lo·Iz Beltagy·Doug Downey·Noah A. Smith
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Abstract

Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task's unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.

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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith. 2020-04-23. Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. https://arxiv.org/abs/2004.10964

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