  With the proliferation of Deep Machine Learning into real-life applications,
a particular property of this technology has been brought to attention:
robustness Neural Networks notoriously present low robustness and can be highly
sensitive to small input perturbations. Recently, many methods for verifying
networks' general properties of robustness have been proposed, but they are
mostly applied in Computer Vision. In this paper we propose a Verification
specification for Natural Language Understanding classification based on larger
regions of interest, and we discuss the challenges of such task. We observe
that, although the data is almost linearly separable, the verifier struggles to
output positive results and we explain the problems and implications.
