The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang, Kyunghyun Cho, Heng Ji, Jiawei Han, Clare R. Voss · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Event schemas encode knowledge of stereotypical structures of events and their connections.As events unfold, schemas are crucial to act as a scaffolding.Previous work on event schema induction focuses either on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and argument relations.We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.In addition, we propose a Temporal Event Graph Model that predicts event instances following the temporal complex event schema.To build and evaluate such schemas, we release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and we have manually constructed gold-standard schemas.Intrinsic evaluations by schema matching and instance graph perplexity, prove the superior quality of our probabilistic graph schema library compared to linear representations.Extrinsic evaluation on schema-guided future event prediction further demonstrates the predictive power of our event graph model, significantly outperforming human schemas and baselines by more than 23.8% on HITS@1. 1