  Scripts - standardized event sequences describing typical everyday activities
- have been shown to help understand narratives by providing expectations,
resolving ambiguity, and filling in unstated information. However, to date they
have proved hard to author or extract from text. In this work, we demonstrate
for the first time that pre-trained neural language models (LMs) can be be
finetuned to generate high-quality scripts, at varying levels of granularity,
for a wide range of everyday scenarios (e.g., bake a cake). To do this, we
collected a large (6.4k), crowdsourced partially ordered scripts (named
proScript), which is substantially larger than prior datasets, and developed
models that generate scripts with combining language generation and structure
prediction. We define two complementary tasks: (i) edge prediction: given a
scenario and unordered events, organize the events into a valid (possibly
partial-order) script, and (ii) script generation: given only a scenario,
generate events and organize them into a (possibly partial-order) script. Our
experiments show that our models perform well (e.g., F1=75.7 in task (i)),
illustrating a new approach to overcoming previous barriers to script
collection. We also show that there is still significant room for improvement
toward human level performance. Together, our tasks, dataset, and models offer
a new research direction for learning script knowledge.
