Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
This work addresses a specific bottleneck in self-supervised language pre-training for NLP researchers, offering an incremental improvement to the MLM framework.
The paper tackles the problem of large gradient variance in Masked Language Models (MLM) due to random masking, proposing a fully-explored masking strategy that divides text into non-overlapping segments and masks tokens within one segment, which consistently outperforms standard random masking in experiments.
Masked Language Model (MLM) framework has been widely adopted for self-supervised language pre-training. In this paper, we argue that randomly sampled masks in MLM would lead to undesirably large gradient variance. Thus, we theoretically quantify the gradient variance via correlating the gradient covariance with the Hamming distance between two different masks (given a certain text sequence). To reduce the variance due to the sampling of masks, we propose a fully-explored masking strategy, where a text sequence is divided into a certain number of non-overlapping segments. Thereafter, the tokens within one segment are masked for training. We prove, from a theoretical perspective, that the gradients derived from this new masking schema have a smaller variance and can lead to more efficient self-supervised training. We conduct extensive experiments on both continual pre-training and general pre-training from scratch. Empirical results confirm that this new masking strategy can consistently outperform standard random masking. Detailed efficiency analysis and ablation studies further validate the advantages of our fully-explored masking strategy under the MLM framework.