  We build on abduction-based explanations for ma-chine learning and develop a
method for computing local explanations for neural network models in natural
language processing (NLP). Our explanations comprise a subset of the words of
the in-put text that satisfies two key features: optimality w.r.t. a
user-defined cost function, such as the length of explanation, and robustness,
in that they ensure prediction invariance for any bounded perturbation in the
embedding space of the left out words. We present two solution algorithms,
respectively based on implicit hitting sets and maximum universal subsets,
introducing a number of algorithmic improvements to speed up convergence of
hard instances. We show how our method can be con-figured with different
perturbation sets in the em-bedded space and used to detect bias in predictions
by enforcing include/exclude constraints on biased terms, as well as to enhance
existing heuristic-based NLP explanation frameworks such as Anchors. We
evaluate our framework on three widely used sentiment analysis tasks and texts
of up to100words from SST, Twitter and IMDB datasets,demonstrating the
effectiveness of the derived explanations.
