The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5203–5215 November 7–11, 2021. c⃝2021 Association for Computational Linguistics 5203 The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction Manling Li1, Sha Li1, Zhenhailong Wang1, Lifu Huang2, Kyunghyun Cho3, Heng Ji1, Jiawei Han1, Clare Voss4 1UIUC 2Virginia Tech 3NYU 4US ARL {manling2,shal2,wangz3,hengji,hanj}@illinois.edu, lifuh@vt.edu, kyunghyun.cho@nyu.edu, clare.r.voss.civ@mail.mil Abstract Event schemas encode knowledge of stereo- typical structures of events and their connec- tions. As events unfold, schemas are cru- cial to act as a scaffolding. Previous work on event schema induction focuses either on atomic events or linear temporal event se- quences, ignoring the interplay between events via arguments and argument relations. We in- troduce a new concept of Temporal Complex Event Schema: a graph-based schema repre- sentation that encompasses events, arguments, temporal connections and argument relations. In addition, we propose a Temporal Event Graph Model that predicts event instances fol- lowing the temporal complex event schema. To build and evaluate such schemas, we re- lease a new schema learning corpus contain- ing 6,399 documents accompanied with event graphs, and we have manually constructed gold-standard schemas. Intrinsic evaluations by schema matching and instance graph per- plexity, prove the superior quality of our prob- abilistic graph schema library compared to linear representations. Extrinsic evaluation on schema-guided future event prediction fur- ther demonstrates the predictive power of our event graph model, significantly outperform- ing human schemas and baselines by more than 23.8% on HITS@1. 1 1 Introduction The current automated event understanding task has been overly simplified to be local and sequen- tial. Real world events, such as disease outbreaks and terrorist attacks, have multiple actors, com- plex timelines, intertwined relations and multiple possible outcomes. Understanding such events re- quires knowledge in the form of a library of event schemas, capturing the progress of time, and per- forming global inference for event prediction. For 1The programs, data and resources are made publicly available for research purpose in https://github.com/ limanling/temporal-graph-schema. example, regarding the 2019 protest in Hong Kong International Airport, a typical question from an- alysts would be “How long will the flights being canceled?” This requires an event understanding system to match events to schema representations and reason about what might happen next. The air- port protest schema would be triggered by “protest” and “flight cancellation”, and evidence of protesters (e.g., the number of protesters, the instruments be- ing used, etc) will suggest a CEO resignation event, or a flight rescheduling event, or continuous flight cancellation events with respective probabilities. Learning Buy Transport Buy Transport Assemble Transport Attack Detonate Crash VEH entity PER learner recepient ORG institute WEA recepient entity entity entity artifact artifact agent Injure Die artifactGPE destination agent instrument place place instrument agent agent located_in FAC located_in agent Figure 1: The example schema of the complex event type car-bombing. A person learned to make bombs and bought materials as well as a vehicle. Then the bomb was assembled to the vehicle, and then the …

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