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.

在自然语言处理模型的可解释性问题上，我们需要定义一个明确的解释定义，以及评估方式，同时需要注意评估指标可能会带来评估偏见，影响解释方法的选择。