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
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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/P18-1175.pdf
Presentation:
 P18-1175.Presentation.pdf
Video:
 https://vimeo.com/285804886
Code
 worksheets/0x900e7e41 +  additional community code