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 taskssuch 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.
The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest . In contrast to human intelligence, these methods lack versatility and struggle to learn and adapt quickly to new tasks , where labeled data is scarce . Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples. In this paper, we propose a meta-learning framework for few-shot word sense disambiguation (WSD), where the goal is to learn 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.
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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
The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest
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such approaches
these methods
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, where labeled data is scarce
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addresses
aims to solve
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a model
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related taskssuch that new tasks can be learned quickly using
few-shot tasks, with an objective to learn new tasks quickly from
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We
In this paper, we
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to learn