Learning Better Masking for Better Language Model Pre-training

• URL: http://arxiv.org/abs/2208.10806v3
• Date: Thu, 25 May 2023 09:05:53 GMT
• Title: Learning Better Masking for Better Language Model Pre-training
• Authors: Dongjie Yang, Zhuosheng Zhang, Hai Zhao
• Abstract summary: Masked Language Modeling has been widely used as denoising objective in pre-training language models (PrLMs) PrLMs commonly adopt a Random-Token Masking strategy where a fixed masking ratio is applied and different contents are masked by an equal probability throughout the entire training. We propose two scheduled masking approaches that adaptively tune the masking ratio and masked content in different training stages.
• Score: 80.31112722910787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Masked Language Modeling (MLM) has been widely used as the denoising objective in pre-training language models (PrLMs). Existing PrLMs commonly adopt a Random-Token Masking strategy where a fixed masking ratio is applied and different contents are masked by an equal probability throughout the entire training. However, the model may receive complicated impact from pre-training status, which changes accordingly as training time goes on. In this paper, we show that such time-invariant MLM settings on masking ratio and masked content are unlikely to deliver an optimal outcome, which motivates us to explore the influence of time-variant MLM settings. We propose two scheduled masking approaches that adaptively tune the masking ratio and masked content in different training stages, which improves the pre-training efficiency and effectiveness verified on the downstream tasks. Our work is a pioneer study on time-variant masking strategy on ratio and content and gives a better understanding of how masking ratio and masked content influence the MLM pre-training.

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