@InProceedings{hancock-EtAl:2018:Long,
  author    = {Hancock, Braden  and  Varma, Paroma  and  Wang, Stephanie  and  Bringmann, Martin  and  Liang, Percy  and  Ré, Christopher},
  title     = {Training Classifiers with Natural Language Explanations},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {1884--1895},
  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.},
  url       = {http://www.aclweb.org/anthology/P18-1175}
}