Revisiting Token Dropping Strategy in Efficient BERT Pretraining
This addresses efficiency and performance issues in BERT pretraining for NLP practitioners, offering an incremental improvement over existing token dropping methods.
The paper tackled the semantic loss problem in token dropping for efficient BERT pretraining, proposing ScTD to preserve semantic information, which saved up to 57% pretraining time and improved performance by up to +1.56% over vanilla token dropping.
Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at several middle layers. It can effectively reduce the training time without degrading much performance on downstream tasks. However, we empirically find that token dropping is prone to a semantic loss problem and falls short in handling semantic-intense tasks. Motivated by this, we propose a simple yet effective semantic-consistent learning method (ScTD) to improve the token dropping. ScTD aims to encourage the model to learn how to preserve the semantic information in the representation space. Extensive experiments on 12 tasks show that, with the help of our ScTD, token dropping can achieve consistent and significant performance gains across all task types and model sizes. More encouragingly, ScTD saves up to 57% of pretraining time and brings up to +1.56% average improvement over the vanilla token dropping.