  Word sense disambiguation (WSD) is a long-standing problem in natural
language processing. One significant challenge in supervised all-words WSD is
to classify among senses for a majority of words that lie in the long-tail
distribution. For instance, 84% of the annotated words have less than 10
examples in the SemCor training data. This issue is more pronounced as the
imbalance occurs in both word and sense distributions. In this work, we propose
MetricWSD, a non-parametric few-shot learning approach to mitigate this data
imbalance issue. By learning to compute distances among the senses of a given
word through episodic training, MetricWSD transfers knowledge (a learned metric
space) from high-frequency words to infrequent ones. MetricWSD constructs the
training episodes tailored to word frequencies and explicitly addresses the
problem of the skewed distribution, as opposed to mixing all the words trained
with parametric models in previous work. Without resorting to any lexical
resources, MetricWSD obtains strong performance against parametric
alternatives, achieving a 75.1 F1 score on the unified WSD evaluation benchmark
(Raganato et al., 2017b). Our analysis further validates that infrequent words
and senses enjoy significant improvement.
