  Preconditions provide a form of logical connection between events that
explains why some events occur together and information that is complementary
to the more widely studied relations such as causation, temporal ordering,
entailment, and discourse relations. Modeling preconditions in text has been
hampered in part due to the lack of large scale labeled data grounded in text.
This paper introduces PeKo, a crowd-sourced annotation of preconditions between
event pairs in newswire, an order of magnitude larger than prior text
annotations. To complement this new corpus, we also introduce two challenge
tasks aimed at modeling preconditions: (i) Precondition Identification -- a
standard classification task defined over pairs of event mentions, and (ii)
Precondition Generation -- a generative task aimed at testing a more general
ability to reason about a given event. Evaluation on both tasks shows that
modeling preconditions is challenging even for today's large language models
(LM). This suggests that precondition knowledge is not easily accessible in
LM-derived representations alone. Our generation results show that fine-tuning
an LM on PeKo yields better conditional relations than when trained on raw text
or temporally-ordered corpora.
