01756nas a2200193   4500008004100000020002200041245008700063210006900150260005200219520106600271100002301337700002301360700002401383700002101407700002501428700002501453700001701478856006701495       2020                        eng d  a978-3-030-61527-700aExplaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars0 aExplaining Sentiment Classification with Synthetic Exemplars and  aChambSpringer International Publishingc2020//3 aWe present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment 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. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability.1 aLampridis, Orestis1 aGuidotti, Riccardo1 aRuggieri, Salvatore1 aAppice, Annalisa1 aTsoumakas, Grigorios1 aManolopoulos, Yannis1 aMatwin, Stan  uhttps://link.springer.com/chapter/10.1007/978-3-030-61527-7_24