  Deep Learning NLP domain lacks procedures for the analysis of model
robustness. In this paper we propose a framework which validates robustness of
any Question Answering model through model explainers. We propose that a robust
model should transgress the initial notion of semantic similarity induced by
word embeddings to learn a more human-like understanding of meaning. We test
this property by manipulating questions in two ways: swapping important
question word for 1) its semantically correct synonym and 2) for word vector
that is close in embedding space. We estimate importance of words in asked
questions with Locally Interpretable Model Agnostic Explanations method (LIME).
With these two steps we compare state-of-the-art Q&A models. We show that
although accuracy of state-of-the-art models is high, they are very fragile to
changes in the input. Moreover, we propose 2 adversarial training scenarios
which raise model sensitivity to true synonyms by up to 7% accuracy measure.
Our findings help to understand which models are more stable and how they can
be improved. In addition, we have created and published a new dataset that may
be used for validation of robustness of a Q&A model.
