  A critical challenge faced by supervised word sense disambiguation (WSD) is
the lack of large annotated datasets with sufficient coverage of words in their
diversity of senses. This inspired recent research on few-shot WSD using
meta-learning. While such work has successfully applied meta-learning to learn
new word senses from very few examples, its performance still lags behind its
fully supervised counterpart. Aiming to further close this gap, we propose a
model of semantic memory for WSD in a meta-learning setting. Semantic memory
encapsulates prior experiences seen throughout the lifetime of the model, which
aids better generalization in limited data settings. Our model is based on
hierarchical variational inference and incorporates an adaptive memory update
rule via a hypernetwork. We show our model advances the state of the art in
few-shot WSD, supports effective learning in extremely data scarce (e.g.
one-shot) scenarios and produces meaning prototypes that capture similar senses
of distinct words.
