  We introduce SelfExplain, a novel self-explaining framework 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 are perceived as more
understandable, adequately justifying and trustworthy by human judges compared
to existing widely-used baselines.
