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arXiv · 1903.05987

To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks

Matthew E. Peters·Sebastian Ruder·Noah A. Smith
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Abstract

While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on the two most common forms of adaptation, feature extraction (where the pretrained weights are frozen), and directly fine-tuning the pretrained model. Our empirical results across diverse NLP tasks with two state-of-the-art models show that the relative performance of fine-tuning vs. feature extraction depends on the similarity of the pretraining and target tasks. We explore possible explanations for this finding and provide a set of adaptation guidelines for the NLP practitioner.

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Matthew E. Peters, Sebastian Ruder, Noah A. Smith. 2019-03-14. To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks. https://arxiv.org/abs/1903.05987

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