Since obtaining a perfect training dataset (i.e., a dataset which is
considerably large, unbiased, and well-representative of unseen cases) is
hardly possible, many real-world text classifiers are trained on the available,
yet imperfect, datasets. These classifiers are thus likely to have undesirable
properties. For instance, they may have biases against some sub-populations or
may not work effectively in the wild due to overfitting. In this paper, we
propose FIND -- a framework which enables humans to debug deep learning text
classifiers by disabling irrelevant hidden features. Experiments show that by
using FIND, humans can improve CNN text classifiers which were trained under
different types of imperfect datasets (including datasets with biases and
datasets with dissimilar train-test distributions).