  Machine learning (ML) models have been applied to a wide range of natural
language processing (NLP) tasks in recent years. In addition to making accurate
decisions, the necessity of understanding how models make their decisions has
become apparent in many applications. To that end, many interpretability
methods that help explain the decision processes of ML models have been
developed. Yet, there currently exists no widely-accepted metric to evaluate
the quality of explanations generated by these methods. As a result, there
currently is no standard way of measuring to what degree an interpretability
method achieves an intended objective. Moreover, there is no accepted standard
of performance by which we can compare and rank the current existing
interpretability methods. In this paper, we propose a novel metric for
quantifying the quality of explanations generated by interpretability methods.
We compute the metric on three NLP tasks using six interpretability methods and
present our results.
