Post-hoc Interpretability for Neural NLP: A Survey

Neural networks for NLP are becoming increasingly complex and widespread, there is a growing concern if these models responsible to use. Explaining helps address the safety ethical concerns essential accountability. Interpretability serves provide explanations in terms that understandable humans. Additionally, post-hoc methods after model learned generally model-agnostic. This survey provides categorization of how recent interpretability communicate humans, it discusses each method in-depth, they validated, as latter often common concern.