  Recent years have witnessed an increasing number of interpretation methods
being developed for improving transparency of NLP models. Meanwhile,
researchers also try to answer the question that whether the obtained
interpretation is faithful in explaining mechanisms behind model prediction?
Specifically, (Jain and Wallace, 2019) proposes that "attention is not
explanation" by comparing attention interpretation with gradient alternatives.
However, it raises a new question that can we safely pick one interpretation
method as the ground-truth? If not, on what basis can we compare different
interpretation methods? In this work, we propose that it is crucial to have a
concrete definition of interpretation before we could evaluate faithfulness of
an interpretation. The definition will affect both the algorithm to obtain
interpretation and, more importantly, the metric used in evaluation. Through
both theoretical and experimental analysis, we find that although
interpretation methods perform differently under a certain evaluation metric,
such a difference may not result from interpretation quality or faithfulness,
but rather the inherent bias of the evaluation metric.
