Natural language often describes events in different granularities, such that
more coarse-grained (goal) events can often be decomposed into fine-grained
sequences of (step) events. A critical but overlooked challenge in
understanding an event process lies in the fact that the step events are not
equally important to the central goal. In this paper, we seek to fill this gap
by studying how well current models can understand the essentiality of
different step events towards a goal event. As discussed by cognitive studies,
such an ability enables the machine to mimic human's commonsense reasoning
about preconditions and necessary efforts of daily-life tasks. Our work
contributes with a high-quality corpus of (goal, step) pairs from a community
guideline website WikiHow, where the steps are manually annotated with their
essentiality w.r.t. the goal. The high IAA indicates that humans have a
consistent understanding of the events. Despite evaluating various statistical
and massive pre-trained NLU models, we observe that existing SOTA models all
perform drastically behind humans, indicating the need for future investigation
of this crucial yet challenging task.

本文研究了机器模型对步骤事件对于目标事件的重要性理解的能力，并利用 WikiHow 提供的高质量数据集进行了测试，结果表明目前最先进的 NLU 模型都不能达到和人类一样的理解水平，需要进一步研究和改进。