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We propose a method to automatically generate a domain- and task-adaptivemaskings of the given text for self-supervised pre-training, such that we caneffectively adapt the language model to a particular target task (e.g. questionanswering). Specifically, we present a novel reinforcement learning-basedframework which learns the masking policy, such that using the generated masksfor further pre-training of the target language model helps improve taskperformance on unseen texts. We use off-policy actor-critic with entropyregularization and experience replay for reinforcement learning, and propose aTransformer-based policy network that can consider the relative importance ofwords in a given text. We validate our Neural Mask Generator (NMG) on severalquestion answering and text classification datasets using BERT and DistilBERTas the language models, on which it outperforms rule-based masking strategies,by automatically learning optimal adaptive maskings.

Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation