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.

翻译：本文提出QABISAR，一种用于法规条文检索的新型框架，旨在解决孤立建模每个查询-法条对时产生的语义失配问题，该问题导致难以学习能有效捕获多方面信息的表示。QABISAR利用查询与法条间的二分交互来捕捉其固有的多样化层面。此外，我们采用知识蒸馏技术，将图网络中增强的查询表示迁移至查询双编码器，从而捕获图表示中蕴含的丰富语义，尽管在推理阶段未见查询缺乏基于图的监督。我们在真实世界专家标注数据集上的实验验证了该框架的有效性。