  Deep neural networks are powerful statistical learners. However, their
predictions do not come with an explanation of their process. To analyze these
models, explanation methods are being developed. We present a novel explanation
method, called OLM, for natural language processing classifiers. This method
combines occlusion and language modeling, which are techniques central to
explainability and NLP, respectively. OLM gives explanations that are
theoretically sound and easy to understand.
  We make several contributions to the theory of explanation methods. Axioms
for explanation methods are an interesting theoretical concept to explore their
basics and deduce methods. We introduce a new axiom, give its intuition and
show it contradicts another existing axiom. Additionally, we point out
theoretical difficulties of existing gradient-based and some occlusion-based
explanation methods in natural language processing. We provide an extensive
argument why evaluation of explanation methods is difficult. We compare OLM to
other explanation methods and underline its uniqueness experimentally. Finally,
we investigate corner cases of OLM and discuss its validity and possible
improvements.
