Masked Language
Masked language modeling (MLM) is a self-supervised learning technique for training language models by masking and predicting words in a sentence. Current research focuses on improving MLM's efficiency and effectiveness through novel masking strategies, enhanced model architectures (like incorporating decoders into encoder-only models), and the development of more robust evaluation metrics for assessing biases and performance across diverse tasks and languages. These advancements are significant because they lead to more accurate and less biased language models with broader applications in natural language processing, including machine translation, text generation, and question answering.
Papers
On Text Style Transfer via Style Masked Language Models
Sharan Narasimhan, Pooja Shekar, Suvodip Dey, Maunendra Sankar Desarkar
MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score
Sunjae Kwon, Zonghai Yao, Harmon S. Jordan, David A. Levy, Brian Corner, Hong Yu