Among these, some works are implicitly on temporal commonsense, such as event durations (Williams, 2012;Vempala et al., 2018), typical temporal ordering (Chklovski and Pantel, 2004;Ning et al., 2018a,b), and script learning (i.e., what happens next after certain events) (Granroth-Wilding and Clark, 2016;Li et al., 2018).
for instance, given two events "going on a vacation" and "going for a walk," most humans would know that a vacation is typically longer and occurs less often than a walk, but it is still challenging for computers to understand and reason about temporal commonsense.