  Gradient-based analysis methods, such as saliency map visualizations and
adversarial input perturbations, have found widespread use in interpreting
neural NLP models due to their simplicity, flexibility, and most importantly,
their faithfulness. In this paper, however, we demonstrate that the gradients
of a model are easily manipulable, and thus bring into question the reliability
of gradient-based analyses. In particular, we merge the layers of a target
model with a Facade that overwhelms the gradients without affecting the
predictions. This Facade can be trained to have gradients that are misleading
and irrelevant to the task, such as focusing only on the stop words in the
input. On a variety of NLP tasks (text classification, NLI, and QA), we show
that our method can manipulate numerous gradient-based analysis techniques:
saliency maps, input reduction, and adversarial perturbations all identify
unimportant or targeted tokens as being highly important. The code and a
tutorial of this paper is available at http://ucinlp.github.io/facade.
