  Word Sense Disambiguation (WSD), which aims to identify the correct sense of
a given polyseme, is a long-standing problem in NLP. In this paper, we propose
to use BERT to extract better polyseme representations for WSD and explore
several ways of combining BERT and the classifier. We also utilize sense
definitions to train a unified classifier for all words, which enables the
model to disambiguate unseen polysemes. Experiments show that our model
achieves the state-of-the-art results on the standard English All-word WSD
evaluation.
