Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play
Sha Li, Revanth Gangi Reddy, Khanh Duy Nguyen, Qingyun Wang, Yi Fung, Chi Han, Jiawei Han, Kartik Natarajan
Abstract
Complex news events, such as natural disasters and socio-political conflicts, require swift responses from the government and society. Relying on historical events to project the future is insufficient as such events are sparse and do not cover all possible conditions and nuanced situations. Simulation of these complex events can help better prepare and reduce the negative impact. We develop a controllable complex news event simulator guided by both the event schema representing domain knowledge about the scenario and user-provided assumptions representing case-specific conditions.As event dynamics depend on the fine-grained social and cultural context, we further introduce a geo-diverse commonsense and cultural norm-aware knowledge enhancement component.To enhance the coherence of the simulation, apart from the global timeline of events,we take an agent-based approach to simulate the individual character states, plans, and actions. By incorporating the schema and cultural norms, our generated simulations achieve much higher coherence and appropriateness and are received favorably by participants from a humanitarian assistance organization.
BibTeX
@inproceedings{li-etal-2024-schema,
title = "Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play",
author = "Li, Sha and
Gangi Reddy, Revanth and
Nguyen, Khanh Duy and
Wang, Qingyun and
Fung, Yi and
Han, Chi and
Han, Jiawei and
Natarajan, Kartik and
Voss, Clare R. and
Ji, Heng",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.39/",
doi = "10.18653/v1/2024.emnlp-demo.39",
pages = "372--381"
}