Iterative Mask Filling: An Effective Text Augmentation Method Using
  Masked Language Modeling
Himmet Toprak Kesgin Mehmet Fatih Amasyali
Data augmentation is an effective technique for improving the performance of
machine learning models. However, it has not been explored as extensively in
natural language processing (NLP) as it has in computer vision. In this paper,
we propose a novel text augmentation method that leverages the Fill-Mask
feature of the transformer-based BERT model. Our method involves iteratively
masking words in a sentence and replacing them with language model predictions.
We have tested our proposed method on various NLP tasks and found it to be
effective in many cases. Our results are presented along with a comparison to
existing augmentation methods. Experimental results show that our proposed
method significantly improves performance, especially on topic classification
datasets.