Deep learning methods typically rely on large amounts of annotated data and do not generalize well to few-shot learning problems where labeled data is scarce. In contrast to human intelligence, such approaches lack versatility and struggle to learn and adapt quickly to new tasks. Meta-learning addresses this problem by training on a large number of related tasks such that new tasks can be learned quickly using a small number of examples. We propose a meta-learning framework for few-shot word sense disambiguation (WSD), where the goal is 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.

本文提出了一个基于元学习的少样本词义消歧框架，用于从极少数标记实例中学习消除未见过单词的歧义。与传统的 Meta-learning 测试方式不同，本研究还探讨了在应对高度不平衡的多类别问题时，几个流行元学习方法在 WSD 任务中的优缺点。

学习学习消歧义：用于少样本词义消歧的元学习