Training Classifiers with Natural Language Explanations
Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, Christopher Ré
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.- Anthology ID:
- P18-1175
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1884–1895
- Language:
- URL:
- https://aclanthology.org/P18-1175
- DOI:
- 10.18653/v1/P18-1175
- Cite (ACL):
- Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, and Christopher Ré. 2018. Training Classifiers with Natural Language Explanations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1884–1895, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Training Classifiers with Natural Language Explanations (Hancock et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/P18-1175.pdf
- Code
- worksheets/0x900e7e41 + additional community code