  To increase trust in artificial intelligence systems, a promising research
direction consists of designing neural models capable of generating natural
language explanations for their predictions. In this work, we show that such
models are nonetheless prone to generating mutually inconsistent explanations,
such as "Because there is a dog in the image" and "Because there is no dog in
the [same] image", exposing flaws in either the decision-making process of the
model or in the generation of the explanations. We introduce a simple yet
effective adversarial framework for sanity checking models against the
generation of inconsistent natural language explanations. Moreover, as part of
the framework, we address the problem of adversarial attacks with full target
sequences, a scenario that was not previously addressed in sequence-to-sequence
attacks. Finally, we apply our framework on a state-of-the-art neural natural
language inference model that provides natural language explanations for its
predictions. Our framework shows that this model is capable of generating a
significant number of inconsistent explanations.
