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 |
Published |
Elsevier Ltd
01.01.2025
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Subjects | |
Online Access | Get full text |
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Summary: | 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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ISSN: | 0306-4573 |
DOI: | 10.1016/j.ipm.2024.103901 |