There is a recent surge of interest in using attention as explanation of
model predictions, with mixed evidence on whether attention can be used as
such. While attention conveniently gives us one weight per input token and is
easily extracted, it is often unclear toward what goal it is used as
explanation. We find that often that goal, whether explicitly stated or not, is
to find out what input tokens are the most relevant to a prediction, and that
the implied user for the explanation is a model developer. For this goal and
user, we argue that input saliency methods are better suited, and that there
are no compelling reasons to use attention, despite the coincidence that it
provides a weight for each input. With this position paper, we hope to shift
some of the recent focus on attention to saliency methods, and for authors to
clearly state the goal and user for their explanations.

本论文论述了在模型解释方面，关注度是否能够用作模型预测的解释存在争议，因为关注度虽然能够在每个输入令牌上提供一个权重，但往往不清楚它用作解释的目标是什么。作者认为，对于寻找与预测结果最相关的输入令牌并且解释的用户是模型开发人员的目标和用户，输入显著性方法更为合适，没有强有力的理由使用关注度。所以，本文提出应将注意力从关注度转向输入显著性方法，并要求作者明确解释的目标和用户。