Few-shot NLP

Few-shot learning is a sub-field of machine learning, which refers to the problem of developing AI models with good performance using only limited ammount of training examples. Few-shot NLP refers to applying few-shot learning to enable natural language processing applications (e.g., sentiment analysis, named entity recognition, etc.) with a few examples.

Approaches

Examples

Intent Classification

Entity Extraction