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It is unclear if existing interpretations of deep neural network modelsrespond effectively to the needs of users. This paper summarizes the commonforms of explanations (such as feature attribution, decision rules, or probes)used in over 200 recent papers about natural language processing (NLP), andcompares them against user questions collected in the XAI Question Bank. Wefound that although users are interested in explanations for the road not taken-- namely, why the model chose one result and not a well-defined, seemlysimilar legitimate counterpart -- most model interpretations cannot answerthese questions.

Explaining the Road Not Taken