Low-shot Learning in Natural Language Processing

Abstract

This paper study the low-shot learning paradigm in Natural Language Processing (NLP), which aims to provide the ability that can adapt to new tasks or new domains with limited annotation data, like zero or few labeled examples. Specifically, Low-shot learning unifies the zero-shot and few-shot learning paradigm. Diverse low-shot learning approaches, including capsule-based networks, data-augmentation methods, and memory networks, are discussed for different NLP tasks, for example, intent detection and named entity typing. We also provide potential future directions for low-shot learning in NLP.