  Natural language expresses events with varying granularities, where
coarse-grained events (goals) can be broken down into finer-grained event
sequences (steps). A critical yet overlooked aspect of understanding event
processes is recognizing that not all step events hold equal importance toward
the completion of a goal. In this paper, we address this gap by examining the
extent to which current models comprehend the essentiality of step events in
relation to a goal event. Cognitive studies suggest that such capability
enables machines to emulate human commonsense reasoning about preconditions and
necessary efforts of everyday tasks. We contribute a high-quality corpus of
(goal, step) pairs gathered from the community guideline website WikiHow, with
steps manually annotated for their essentiality concerning the goal by experts.
The high inter-annotator agreement demonstrates that humans possess a
consistent understanding of event essentiality. However, after evaluating
multiple statistical and largescale pre-trained language models, we find that
existing approaches considerably underperform compared to humans. This
observation highlights the need for further exploration into this critical and
challenging task. The dataset and code are available at
http://cogcomp.org/page/publication_view/1023.
