Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100× faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.
REF proposes to use a classifier with natural language explanations that are annotated by human beings to do the classification.
Training Classifiers with Natural Language Explanations
Proceedings of the conference. Association for Computational Linguistics. Meeting
Proceedings of the conference. Association for Computational Linguistics. Meeting
Computer Science Medicine