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In recent years, several methods have been proposed for explaining individualpredictions of deep learning models, yet there has been little study of how toaggregate these predictions to explain how such models view classes as a wholein text classification tasks. In this work, we propose a method for explainingclasses using deep learning models and the Integrated Gradients featureattribution technique by aggregating explanations of individual examples intext classification to general descriptions of the classes. We demonstrate theapproach on Web register (genre) classification using the XML-R model and theCorpus of Online Registers of English (CORE), finding that the methodidentifies plausible and discriminative keywords characterizing all but thesmallest class.

Explaining Classes through Word Attribution