We present XSPELLS, a model-agnostic local approach for explaining the decisions of black box models in classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. XSPELLS generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. Moreover, diversity of counter-exemplars is modeled as an optimization problem, solved by a greedy algorithm with theoretical guarantee. We report experiments on three datasets showing that XSPELLS outperforms the well-known LIME method in terms of quality of explanations, fidelity, diversity, and usefulness, and that is comparable to it in terms of stability.
