Abstract
Recently, the rich content of Chinese legal documents has attracted considerable scholarly attention. Legal Relational Triple Extraction which is a critical way to enable machines to understand the semantic information presents a significant challenge in Natural Language Processing, as it seeks to discern the connections between pairs of entities within legal case texts. This challenge is compounded by the intricate nature of legal language and the substantial expense associated with human annotation. Despite these challenges, existing models often overlook the incorporation of cross-domain features. To address this, we introduce LegalATLE, an innovative method for legal Relational Triple Extraction that integrates active learning and transfer learning, reducing the model’s reliance on annotated data and enhancing its performance within the target domain. Our model employs active learning to prudently assess and select samples with high information value. Concurrently, it applies domain adaptation techniques to effectively transfer knowledge from the source domain, thereby improving the model’s generalization and accuracy. Additionally, we have manually annotated a new theft-related triple dataset for use as the target domain. Comprehensive experiments demonstrate that LegalATLE outperforms existing efficient models by approximately 1.5%, reaching 92.90% on the target domain. Notably, with only 4% and 5% of the full dataset used for training, LegalATLE performs about 10% better than other models, demonstrating its effectiveness in data-scarce scenarios.
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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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04 November 2024
A Correction to this paper has been published: https://doi.org/10.1007/s10489-024-05844-w
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Acknowledgements
This work is supported by the National Key Research and Development Program of China (No. 2022YFC3301801), the Fundamental Research Funds for the Central Universities (No. DUT22ZD205).
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Conceptualization: Haiguang Zhang and Yuanyuan Sun; Methodology: Haiguang Zhang and Yuanyuan Sun; Formal analysis and investigation: Haiguang Zhang, Yuanyuan Sun and Bo Xu; Writing - original draft preparation: Haiguang Zhang, and Bo Xu; Writing - review and editing: Yuanyuan Sun, Bo Xu and Hongfei Lin; Supervision: Yuanyuan Sun, Bo Xu and Hongfei Lin.
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Zhang, H., Sun, Y., Xu, B. et al. LegalATLE: an active transfer learning framework for legal triple extraction. Appl Intell 54, 12835–12850 (2024). https://doi.org/10.1007/s10489-024-05842-y
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DOI: https://doi.org/10.1007/s10489-024-05842-y