Natural language processing (NLP) models depend heavily on data, but obtaining high-quality labeled data at scale is one of the biggest hurdles. It quickly becomes clear that throwing more raw data at an NLP problem doesn’t really help much – it’s the labeled data that really drives improvement. This is where active learning and a human-in-the-loop approach become invaluable. They help us prioritize which data to label, involve human expertise at critical points, and continuously improve models in production.
In this article, we’ll talk about what active learning is, how to implement a human-in-the-loop workflow for NLP annotation, and why this approach accelerates model improvement.