  To demystify the "black box" property of deep neural networks for natural
language processing (NLP), several methods have been proposed to interpret
their predictions by measuring the change in prediction probability after
erasing each token of an input. Since existing methods replace each token with
a predefined value (i.e., zero), the resulting sentence lies out of the
training data distribution, yielding misleading interpretations. In this study,
we raise the out-of-distribution problem induced by the existing interpretation
methods and present a remedy; we propose to marginalize each token out. We
interpret various NLP models trained for sentiment analysis and natural
language inference using the proposed method.
