  Due to the black-box nature of deep learning models, methods for explaining
the models' results are crucial to gain trust from humans and support
collaboration between AIs and humans. In this paper, we consider several
model-agnostic and model-specific explanation methods for CNNs for text
classification and conduct three human-grounded evaluations, focusing on
different purposes of explanations: (1) revealing model behavior, (2)
justifying model predictions, and (3) helping humans investigate uncertain
predictions. The results highlight dissimilar qualities of the various
explanation methods we consider and show the degree to which these methods
could serve for each purpose.
