POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events
Sai Vallurupalli, Sayontan Ghosh, Katrin Erk, Niranjan Balasubramanian, Francis Ferraro
Abstract
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, crowdworkers are ableto (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 theoutcome 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 of8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96weighted Fleiss Kappa). Our dataset, POQUe (Participant Outcome Questions), enables theexploration 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.
BibTeX
@inproceedings{vallurupalli-etal-2022-poque,
title = "{POQ}ue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events",
author = "Vallurupalli, Sai and
Ghosh, Sayontan and
Erk, Katrin and
Balasubramanian, Niranjan and
Ferraro, Francis",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.594/",
doi = "10.18653/v1/2022.emnlp-main.594",
pages = "8674--8697"
}