Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation

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.