Post-hoc Interpretability for Neural NLP: A Survey
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Natural Language Processing (NLP) models have become increasingly more complex and widespread. With recent developments in neural networks, a growing concern is whether it is responsible to use these models. Concerns such as safety and ethics can be partially addressed by providing explanations. Furthermore, when models do fail, providing explanations is paramount for accountability purposes. To this end, interpretability serves to provide these explanations in terms that are understandable to humans. Central to what is understandable is how explanations are communicated. Therefore, this survey provides a categorization of how recent interpretability methods communicate explanations and discusses the methods in depth. Furthermore, the survey focuses on post-hoc methods, which provide explanations after a model is learned and generally model-agnostic. A common concern for this class of methods is whether they accurately reflect the model. Hence, how these post-hoc methods are evaluated is discussed throughout the paper.
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