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Event-Event Relation Extraction using Probabilistic Box Embedding

Cited 6 time in Web of Science Cited 11 time in Scopus

Hwang, EunJeong; Lee, Jay-Yoon; Yang, Tianyi; Patel, Dhruvesh; Zhang, Dongxu; McCallum, Andrew

Issue Date
Association for Computational Linguistics (ACL)
Proceedings of the Annual Meeting of the Association for Computational Linguistics, Vol.2, pp.235-244
To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) frameworks regard it as a multi-class classification task, and do not guarantee any coherence between different relation types. For instance, if a phone line died after storm, then it is evident that the storm happened before the died. Current frameworks of event relation extraction do not guarantee this anti-symmetry and thus enforce it via a constraint loss function (Wang et al., 2020). In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints. Our experiments show that BERE has stronger conjunctive constraint satisfaction while performing on par or better in terms of F1 compared to previous models with constraint injection.
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  • Graduate School of Data Science
Research Area Constraint injection, Energy-based models, Structured Prediction


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