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With the growing popularity of deep-learning based NLP models, comes a needfor interpretable systems. But what is interpretability, and what constitutes ahigh-quality interpretation? In this opinion piece we reflect on the currentstate of interpretability evaluation research. We call for more clearlydifferentiating between different desired criteria an interpretation shouldsatisfy, and focus on the faithfulness criteria. We survey the literature withrespect to faithfulness evaluation, and arrange the current approaches aroundthree assumptions, providing an explicit form to how faithfulness is "defined"by the community. We provide concrete guidelines on how evaluation ofinterpretation methods should and should not be conducted. Finally, we claimthat the current binary definition for faithfulness sets a potentiallyunrealistic bar for being considered faithful. We call for discarding thebinary notion of faithfulness in favor of a more graded one, which we believewill be of greater practical utility.

Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?