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Through exploiting a high level of parallelism enabled by graphics processingunits, transformer architectures have enabled tremendous strides forward in thefield of natural language processing. In a traditional masked language model,special MASK tokens are used to prompt our model to gather contextualinformation from surrounding words to restore originally hidden information. Inthis paper, we explore a task-specific masking framework for pre-trained largelanguage models that enables superior performance on particular downstreamtasks on the datasets in the GLUE benchmark. We develop our own maskingalgorithm, Typhoon, based on token input gradients, and compare this with otherstandard baselines. We find that Typhoon offers performance competitive withwhole-word masking on the MRPC dataset. Our implementation can be found in apublic Github Repository.

Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models