Few-shot cross domain event discovery in narrative text
Cross-domain event detection presents notable challenges in the form of data scarcity, and existing few-shot algorithms only consider events whose types are predefined, resulting in low coverage or excessive trivial identification results. To address this issue, this paper proposes the task Few-shot...
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Published in | Information processing & management Vol. 62; no. 1; p. 103901 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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Elsevier Ltd
01.01.2025
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Abstract | Cross-domain event detection presents notable challenges in the form of data scarcity, and existing few-shot algorithms only consider events whose types are predefined, resulting in low coverage or excessive trivial identification results. To address this issue, this paper proposes the task Few-shot Cross Domain Event Discovery, which includes two subtasks: Domain Event Discovery and Few-shot Domain Adaptation. The former aims to identify the type-agnostic event triggers, and the latter completes domain adaptation with only a few annotated domain samples. Additionally, we introduce a positive–negative balanced sampling mechanism and a novel domain parameter adapter for these two subtasks, respectively. Extensive experiments on the DuEE dataset and the ACE2005 dataset show that our proposed method outperforms the current state-of-the-art method by 6.3% in Mix-F1 score on average. Moreover, we achieve SOTA performance in all domains of the DuEE dataset.
•Define and formulate the new task Few-shot Cross Domain Event Discovery.•Propose positive–negative balanced sampling mechanism and domain parameter adapter.•Experimental results show the effectiveness and application potential of our method. |
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AbstractList | Cross-domain event detection presents notable challenges in the form of data scarcity, and existing few-shot algorithms only consider events whose types are predefined, resulting in low coverage or excessive trivial identification results. To address this issue, this paper proposes the task Few-shot Cross Domain Event Discovery, which includes two subtasks: Domain Event Discovery and Few-shot Domain Adaptation. The former aims to identify the type-agnostic event triggers, and the latter completes domain adaptation with only a few annotated domain samples. Additionally, we introduce a positive–negative balanced sampling mechanism and a novel domain parameter adapter for these two subtasks, respectively. Extensive experiments on the DuEE dataset and the ACE2005 dataset show that our proposed method outperforms the current state-of-the-art method by 6.3% in Mix-F1 score on average. Moreover, we achieve SOTA performance in all domains of the DuEE dataset.
•Define and formulate the new task Few-shot Cross Domain Event Discovery.•Propose positive–negative balanced sampling mechanism and domain parameter adapter.•Experimental results show the effectiveness and application potential of our method. |
ArticleNumber | 103901 |
Author | Zhu, Yongkai Zhang, Jinpeng Huang, Fei Ma, Jinsong Bai, Jie He, Ruifang Zhang, Shiqi |
Author_xml | – sequence: 1 givenname: Ruifang surname: He fullname: He, Ruifang email: rfhe@tju.edu.cn organization: College of Intelligence and Computing, Tianjin University, Yaguan Road, Jinnan District, Tianjin, 300400, China – sequence: 2 givenname: Fei surname: Huang fullname: Huang, Fei email: feihuang@tju.edu.cn organization: College of Intelligence and Computing, Tianjin University, Yaguan Road, Jinnan District, Tianjin, 300400, China – sequence: 3 givenname: Jinsong surname: Ma fullname: Ma, Jinsong email: jsma@tju.edu.cn organization: College of Intelligence and Computing, Tianjin University, Yaguan Road, Jinnan District, Tianjin, 300400, China – sequence: 4 givenname: Jinpeng surname: Zhang fullname: Zhang, Jinpeng email: zjpbinary@tju.edu.cn organization: College of Intelligence and Computing, Tianjin University, Yaguan Road, Jinnan District, Tianjin, 300400, China – sequence: 5 givenname: Yongkai surname: Zhu fullname: Zhu, Yongkai email: zhuyk@cnooc.com.cn organization: CNOOC Energy Technology & Services-Oil Production Services Co, China – sequence: 6 givenname: Shiqi surname: Zhang fullname: Zhang, Shiqi email: w08080@cetc.com.cn organization: The 54th Research Institute of CETC, China – sequence: 7 givenname: Jie surname: Bai fullname: Bai, Jie email: w08082@cetc.com.cn organization: The 54th Research Institute of CETC, China |
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Keywords | Event discovery Few-shot domain adaptation Positive–negative balanced sampling Parameter adapter |
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