Training Classifiers with Natural Language Explanations

Code

• worksheets.codalab.org/worksheets/0x900e7e41deaa4ec5b2fe41dc50594548
  Officialnone★ 0
• github.com/MurtyShikhar/ExpBERT
  pytorch★ 29

Abstract

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.

Tasks

Reproductions