用RankSVM优化法律案例检索排名，提升准确率并缓解类别不平衡问题。
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM
- 以RankSVM替代全连接层，采用成对学习方式优化排序
- 在LeCaRDv1和v2数据集上均实现排名性能提升
- 适合关注法律AI排序优化与过拟合控制的研究者
随着法律AI的快速发展，相似案例检索成为重要研究任务，尤其关注语言模型的应用。本文从学习排序角度出发，尝试改进现有模型的排序性能，而非依赖语言模型。具体地，在LeCaRDv1和LeCaRDv2数据集上，采用成对方法RankSVM作为分类器，替代原有的全连接层，并结合常用语言模型进行实验。结果表明，与原始分类器相比，RankSVM能普遍提升检索性能，优化排名准确性，同时有助于缓解因类别不平衡导致的过拟合问题。代码已公开于https://github.com/liuyuqi123study/RankSVM_for_SLR。
原文摘要 · Abstract (English)
Given the rapid development of Legal AI, a lot of attention has been paid to one of the most important legal AI tasks--similar case retrieval, especially with language models to use. In our paper, however, we try to improve the ranking performance of current models from the perspective of learning to rank instead of language models. Specifically, we conduct experiments using a pairwise method--RankSVM as the classifier to substitute a fully connected layer, combined with commonly used language models on similar case retrieval datasets LeCaRDv1 and LeCaRDv2. We finally come to the conclusion that RankSVM could generally help improve the retrieval performance on the LeCaRDv1 and LeCaRDv2 datasets compared with original classifiers by optimizing the precise ranking. It could also help mitigate overfitting owing to class imbalance. Our code is available in https://github.com/liuyuqi123study/RankSVM_for_SLR
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