Event Schema Induction with Double Graph Autoencoders

Xiaomeng Jin, Manling Li, Heng Ji


Abstract
Event schema depicts the typical structure of complex events, serving as a scaffolding to effectively analyze, predict, and possibly intervene in the ongoing events. To induce event schemas from historical events, previous work uses an event-by-event scheme, ignoring the global structure of the entire schema graph. We propose a new event schema induction framework using double graph autoencoders, which captures the global dependencies among nodes in event graphs. Specifically, we first extract the event skeleton from an event graph and design a variational directed acyclic graph (DAG) autoencoder to learn its global structure. Then we further fill in the event arguments for the skeleton, and use another Graph Convolutional Network (GCN) based autoencoder to reconstruct entity-entity relations as well as to detect coreferential entities. By performing this two-stage induction decomposition, the model can avoid reconstructing the entire graph in one step, allowing it to focus on learning global structures between events. Experimental results on three event graph datasets demonstrate that our method achieves state-of-the-art performance and induces high-quality event schemas with global consistency.
Anthology ID:
2022.naacl-main.147
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2013–2025
Language:
URL:
https://aclanthology.org/2022.naacl-main.147
DOI:
10.18653/v1/2022.naacl-main.147
Bibkey:
Cite (ACL):
Xiaomeng Jin, Manling Li, and Heng Ji. 2022. Event Schema Induction with Double Graph Autoencoders. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2013–2025, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Event Schema Induction with Double Graph Autoencoders (Jin et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.147.pdf
Software:
 2022.naacl-main.147.software.zip