  This paper studies a new cognitively motivated semantic typing task,
multi-axis event process typing, that, given an event process, attempts to
infer free-form type labels describing (i) the type of action made by the
process and (ii) the type of object the process seeks to affect. This task is
inspired by computational and cognitive studies of event understanding, which
suggest that understanding processes of events is often directed by recognizing
the goals, plans or intentions of the protagonist(s). We develop a large
dataset containing over 60k event processes, featuring ultra fine-grained
typing on both the action and object type axes with very large ($10^3\sim
10^4$) label vocabularies. We then propose a hybrid learning framework, P2GT,
which addresses the challenging typing problem with indirect supervision from
glosses1and a joint learning-to-rank framework. As our experiments indicate,
P2GT supports identifying the intent of processes, as well as the fine semantic
type of the affected object. It also demonstrates the capability of handling
few-shot cases, and strong generalizability on out-of-domain event processes.
