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Recent years have seen important advances in the quality of state-of-the-artmodels, but this has come at the expense of models becoming less interpretable.This survey presents an overview of the current state of Explainable AI (XAI),considered within the domain of Natural Language Processing (NLP). We discussthe main categorization of explanations, as well as the various waysexplanations can be arrived at and visualized. We detail the operations andexplainability techniques currently available for generating explanations forNLP model predictions, to serve as a resource for model developers in thecommunity. Finally, we point out the current gaps and encourage directions forfuture work in this important research area.

A Survey of the State of Explainable AI for Natural Language Processing