  Domain adaptation or transfer learning using pre-trained language models such
as BERT has proven to be an effective approach for many natural language
processing tasks. In this work, we propose to formulate word sense
disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair
ranking task to select the most probable sense definition given a context
sentence and a list of candidate sense definitions. We also introduce a data
augmentation technique for WSD using existing example sentences from WordNet.
Using the proposed training objective and data augmentation technique, our
models are able to achieve state-of-the-art results on the English all-words
benchmark datasets.

Computation and Language;Machine Learning