Explore an explanation-based learning framework for natural language processing that improves label efficiency and model reliability in this 45-minute conference talk. Discover how human explanations can be leveraged to create more effective NLP systems with fewer training examples compared to traditional deep learning approaches. Learn about techniques like neural rule grounding and soft rule matching that allow models to generalize from explanations. Examine case studies demonstrating improved performance on tasks like relation extraction and hate speech classification using this framework. Gain insights into making NLP model development more accessible and less reliant on large labeled datasets or machine learning expertise.