POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events
This work addresses the challenge of semantic understanding in NLP by providing a dataset for developing models that comprehend complex events, outcomes, and participant influence, though it is incremental as it builds on existing annotation methods.
The paper tackled the problem of acquiring knowledge about outcomes for complex event understanding by pre-identifying participants, enabling crowd workers to infer collective impact, annotate volitional engagement, and ground outcomes in state changes, resulting in a high-quality annotated dataset of 8K narratives with inter-annotator agreement of 0.74-0.96 weighted Fleiss Kappa.
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.