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Deep neural networks are powerful statistical learners. However, theirpredictions do not come with an explanation of their process. To analyze thesemodels, explanation methods are being developed. We present a novel explanationmethod, called OLM, for natural language processing classifiers. This methodcombines occlusion and language modeling, which are techniques central toexplainability and NLP, respectively. OLM gives explanations that aretheoretically sound and easy to understand.  We make several contributions to the theory of explanation methods. Axiomsfor explanation methods are an interesting theoretical concept to explore theirbasics and deduce methods. We introduce a new axiom, give its intuition andshow it contradicts another existing axiom. Additionally, we point outtheoretical difficulties of existing gradient-based and some occlusion-basedexplanation methods in natural language processing. We provide an extensiveargument why evaluation of explanation methods is difficult. We compare OLM toother explanation methods and underline its uniqueness experimentally. Finally,we investigate corner cases of OLM and discuss its validity and possibleimprovements.

Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling