Recall and Learn: Fine tuning Deep Pretrained Language Models with Less Forgetting
Deep pretrained language models have achieved great success in the way of pretraining first and then fine tuning. But such a sequential transfer learning paradigm often confronts the catastrophic forgetting problem and leads to sub optimal performance. To fine tune with less forgetting, we propose a recall and learn mechanism, which adopts the idea of multi task learning and jointly learns pretraining tasks and downstream tasks. Specifically, we propose a Pretraining Simulation mechanism to recall the knowledge from pretraining tasks without data, and an Objective Shifting mechanism to focus the learning on downstream tasks gradually. Experiments show that our method achieves state of the art performance on the GLUE benchmark. Our method also enables BERT base to achieve better performance than directly fine tuning of BERT large. Further, we provide the open source RecAdam optimizer, which integrates the proposed mechanisms into Adam optimizer, to facility the NLP community.
EMNLP