SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers
Dheeraj Rajagopal, Vidhisha Balachandran, Eduard H Hovy, Yulia Tsvetkov
Abstract
We introduce SelfExplain, a novel self-explaining model that explains a text classifier’s predictions using phrase-based concepts. SelfExplain augments existing neural classifiers by adding (1) a globally interpretable layer that identifies the most influential concepts in the training set for a given sample and (2) a locally interpretable layer that quantifies the contribution of each local input concept by computing a relevance score relative to the predicted label. Experiments across five text-classification datasets show that SelfExplain facilitates interpretability without sacrificing performance. Most importantly, explanations from SelfExplain show sufficiency for model predictions and are perceived as adequate, trustworthy and understandable by human judges compared to existing widely-used baselines.- Anthology ID:
- 2021.emnlp-main.64
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 836–850
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.64
- DOI:
- 10.18653/v1/2021.emnlp-main.64
- Cite (ACL):
- Dheeraj Rajagopal, Vidhisha Balachandran, Eduard H Hovy, and Yulia Tsvetkov. 2021. SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 836–850, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers (Rajagopal et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.emnlp-main.64.pdf
- Code
- dheerajrajagopal/SelfExplain + additional community code
- Data
- SST, SST-2, SST-5