SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers
Dheeraj Rajagopal, Vidhisha Balachandran, Eduard H. Hovy, Yulia Tsvetkov · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
We introduce SELFEXPLAIN, a novel selfexplaining 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 SELFEX-PLAIN 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.1