  The success of deep learning methods hinges on the availability of large
training datasets annotated for the task of interest. In contrast to human
intelligence, these methods lack versatility and struggle to learn and adapt
quickly to new tasks, where labeled data is scarce. Meta-learning aims to solve
this problem by training a model on a large number of few-shot tasks, with an
objective to learn new tasks quickly from a small number of examples. In this
paper, we propose a meta-learning framework for few-shot word sense
disambiguation (WSD), where the goal is to learn to disambiguate unseen words
from only a few labeled instances. Meta-learning approaches have so far been
typically tested in an $N$-way, $K$-shot classification setting where each task
has $N$ classes with $K$ examples per class. Owing to its nature, WSD deviates
from this controlled setup and requires the models to handle a large number of
highly unbalanced classes. We extend several popular meta-learning approaches
to this scenario, and analyze their strengths and weaknesses in this new
challenging setting.
