  Word Sense Disambiguation (WSD) is an NLP task aimed at determining the
correct sense of a word in a sentence from discrete sense choices. Although
current systems have attained unprecedented performances for such tasks, the
nonuniform distribution of word senses during training generally results in
systems performing poorly on rare senses. To this end, we consider data
augmentation to increase the frequency of these least frequent senses (LFS) to
reduce the distributional bias of senses during training. We propose
Sense-Maintained Sentence Mixup (SMSMix), a novel word-level mixup method that
maintains the sense of a target word. SMSMix smoothly blends two sentences
using mask prediction while preserving the relevant span determined by saliency
scores to maintain a specific word's sense. To the best of our knowledge, this
is the first attempt to apply mixup in NLP while preserving the meaning of a
specific word. With extensive experiments, we validate that our augmentation
method can effectively give more information about rare senses during training
with maintained target sense label.
