{"type":"ScholarlyArticle","title":"Incorporating Schema-Aware Description into Document-Level Event Extraction","authors":[{"name":"Zijie Xu","slug":"zijie-xu","given":"Zijie","family":"Xu","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Peng Wang","slug":"peng-wang","given":"Peng","family":"Wang","affiliation":"School of Computer Science and Engineering , Southeast University; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education"},{"name":"Wenjun Ke","slug":"wenjun-ke","given":"Wenjun","family":"Ke","affiliation":"School of Computer Science and Engineering , Southeast University; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education"},{"name":"Guozheng Li","slug":"guozheng-li","given":"Guozheng","family":"Li","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Jiajun Liu","slug":"jiajun-liu","given":"Jiajun","family":"Liu","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Ke Ji","slug":"ke-ji","given":"Ke","family":"Ji","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Xiye Chen","slug":"xiye-chen","given":"Xiye","family":"Chen","affiliation":"Nanjing University of Finance and Economics"},{"name":"Chenxiao Wu","slug":"chenxiao-wu","given":"Chenxiao","family":"Wu","affiliation":"School of Computer Science and Engineering , Southeast University"}],"abstract":"Document-level event extraction (DEE) aims to extract the structured event information from a given document, facing two critical challenges: (1) event arguments always scatter across sentences (arguments-scattering); (2) multiple events can co-occur in one document (multi-event). Most recent studies mainly follow two simplified settings to ease the challenges: one simplifies DEE with the no-trigger-words design (NDEE), and the other focuses on event argument extraction (DEAE), a sub-task of DEE. However, the former excludes trigger extraction and suffers from error propagation in the sub-tasks. The latter relies heavily on the gold triggers as prerequisites and struggles to distinguish multiple arguments playing the same role in different events. To address the limitations above, we propose a novel joint trigger and argument extraction paradigm SEELE to enhance the DEE model via incorporating SchEma-awarE descriptions into Document-Level Event extraction. Specifically, the schema-aware descriptions are leveraged from two aspects: (1) guiding the attention mechanism among event-aware tokens across sentences, which relieves arguments-scattering without error propagation; (2) performing the fine-grained contrastive learning to distinguish different events, which mitigates multi-event without gold triggers. Extensive experiments show the superiority of SEELE, achieving notable improvements (2.1% to 9.7% F1) on three NDEE datasets and competitive performance on two DEAE datasets. Our code is available at https://github.com/TheoryRhapsody/SEELE.","doi":"10.24963/ijcai.2024/729","url":"https://ijcai.org/proceedings/2024/729","pdf":"https://ijcai.org/proceedings/2024/0729.pdf","markdown":"https://ijcai.org/proceedings/2024/729.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/729","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":729,"pages":{"first":6597,"last":6605},"keywords":["Natural Language Processing: NLP: Information extraction"],"videos":[],"references":[{"position":1,"raw":"[Anantheswaran et al., 2023] Ujjwala Anantheswaran, Hi- manshu Gupta, Mihir Parmar, Kuntal Kumar Pal, and Chitta Baral. 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