CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification

• Main Authors: CHEN, Meiqi, CAO, Yixin, ZHANG, Yan, LIU, Zhiwei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: benchmark datasets; causal relations; coreference; high-order; higher-order; identification modeling; interaction graphs; level graphs; multitask learning; ordering events; Computer Sciences; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/8287; https://ink.library.smu.edu.sg/context/sis_research/article/9290/viewcontent/2023.acl_long.604__1_.pdf
• Institution: Singapore Management University

Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail...