CaseGNN++: Graph Contrastive Learning for Legal Case Retrieval with Graph Augmentation
arxiv(2024)
摘要
Legal case retrieval (LCR) is a specialised information retrieval task that
aims to find relevant cases to a given query case. LCR holds pivotal
significance in facilitating legal practitioners in finding precedents. Most of
existing LCR methods are based on traditional lexical models and language
models, which have gained promising performance in retrieval. However, the
domain-specific structural information inherent in legal documents is yet to be
exploited to further improve the performance. Our previous work CaseGNN
successfully harnesses text-attributed graphs and graph neural networks to
address the problem of legal structural information neglect. Nonetheless, there
remain two aspects for further investigation: (1) The underutilization of rich
edge information within text-attributed case graphs limits CaseGNN to generate
informative case representation. (2) The inadequacy of labelled data in legal
datasets hinders the training of CaseGNN model. In this paper, CaseGNN++, which
is extended from CaseGNN, is proposed to simultaneously leverage the edge
information and additional label data to discover the latent potential of LCR
models. Specifically, an edge feature-based graph attention layer (EUGAT) is
proposed to comprehensively update node and edge features during graph
modelling, resulting in a full utilisation of structural information of legal
cases. Moreover, a novel graph contrastive learning objective with graph
augmentation is developed in CaseGNN++ to provide additional training signals,
thereby enhancing the legal comprehension capabilities of CaseGNN++ model.
Extensive experiments on two benchmark datasets from COLIEE 2022 and COLIEE
2023 demonstrate that CaseGNN++ not only significantly improves CaseGNN but
also achieves supreme performance compared to state-of-the-art LCR methods.
Code has been released on https://github.com/yanran-tang/CaseGNN.
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