通过查询-法条双路交互提升法律条文检索精度
QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval
- 构建查询与法条的双路交互机制，捕捉多维度语义
- 利用知识蒸馏将图网络中的丰富语义迁移到编码器
- 在真实标注数据集上显著优于现有方法
本文提出QABISAR框架用于法律条文检索，以解决孤立建模查询-法条对时存在的语义不匹配问题，该问题导致难以学习到能捕捉多方面信息的表示。QABISAR通过查询与法条间的二部图交互，捕获其内在的多样化特征。此外，采用知识蒸馏技术，将图网络中生成的丰富查询表示迁移至查询双编码器，从而在推理阶段无图监督的情况下仍能捕获图表示中的丰富语义。在真实世界专家标注的数据集上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
In this paper, we introduce QABISAR, a novel framework for statutory article retrieval, to overcome the semantic mismatch problem when modeling each query-article pair in isolation, making it hard to learn representation that can effectively capture multi-faceted information. QABISAR leverages bipartite interactions between queries and articles to capture diverse aspects inherent in them. Further, we employ knowledge distillation to transfer enriched query representations from the graph network into the query bi-encoder, to capture the rich semantics present in the graph representations, despite absence of graph-based supervision for unseen queries during inference. Our experiments on a real-world expert-annotated dataset demonstrate its effectiveness.
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