ERGO: Event relational graph transformer for document-level event causality identification

• Main Authors: CHEN, Meiqi, CAO, Yixin, DENG, Kunquan, LI, Mukai, WANG, Kun, SHAO, Jing, ZHANG, Yan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/7451; https://ink.library.smu.edu.sg/context/sis_research/article/8454/viewcontent/2022.coling_1.185.pdf
• Institution: Singapore Management University

Document-level Event Causality Identification (DECI) aims to identify event-event causal relations in a document. Existing works usually build an event graph for global reasoning across multiple sentences. However, the edges between events have to be carefully designed through heuristic rules or ext...