  Knowledge about outcomes is critical for complex event understanding but is
hard to acquire. We show that by pre-identifying a participant in a complex
event, crowd workers are able to (1) infer the collective impact of salient
events that make up the situation, (2) annotate the volitional engagement of
participants in causing the situation, and (3) ground the outcome of the
situation in state changes of the participants. By creating a multi-step
interface and a careful quality control strategy, we collect a high quality
annotated dataset of 8K short newswire narratives and ROCStories with high
inter-annotator agreement (0.74-0.96 weighted Fleiss Kappa). Our dataset, POQue
(Participant Outcome Questions), enables the exploration and development of
models that address multiple aspects of semantic understanding. Experimentally,
we show that current language models lag behind human performance in subtle
ways through our task formulations that target abstract and specific
comprehension of a complex event, its outcome, and a participant's influence
over the event culmination.
