To Tune Or Not To Tune? Adapting Pretrained Representations To Diverse Tasks
Matthew E. Peters, Sebastian Ruder, Noah A. Smith . Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) 2019 – 398 citations
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.