  The goal of Word Sense Disambiguation (WSD) is to identify the sense of a
polysemous word in a specific context. Deep-learning techniques using BERT have
achieved very promising results in the field and different methods have been
proposed to integrate structured knowledge to enhance performance. At the same
time, an increasing number of data augmentation techniques have been proven to
be useful for NLP tasks. Building upon previous works leveraging BERT and
WordNet knowledge, we explore different data augmentation techniques on
context-gloss pairs to improve the performance of WSD. In our experiment, we
show that both sentence-level and word-level augmentation methods are effective
strategies for WSD. Also, we find out that performance can be improved by
adding hypernyms' glosses obtained from a lexical knowledge base. We compare
and analyze different context-gloss augmentation techniques, and the results
show that applying back translation on gloss performs the best.
