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.

本文提出了一种基于 MetricWSD 的非参数的少样本学习方法，通过进行情节性训练，并学习计算给定单词的不同意义之间的距离，将高频词的学习度量空间转移到低频的单词中，从而避免了在自然语言处理中监督式全单词 WSD 的一个重要挑战中的数据不平衡问题。该方法在不依赖任何词汇资源的情况下，能够获得强大的性能，并通过联合 WSD 评估基准实现了 75.1 F1 得分的良好表现。