Ai+ Training

Humans can often learn how to detect new entities and categorize documents from just a handful of examples, yet existing natural language processing (NLP) systems typically require tens of thousands of examples to perform with similar accuracy. While deep learning methods have achieved impressive results on academic datasets, these data-hungry models are expensive to build and hard to maintain. In this talk, I will present an explanation-based learning framework that can make the process of building and maintaining NLP models more label efficient and reliable, and less reliant on machine learning expertise.