QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval
This addresses retrieval challenges for legal professionals, but it appears incremental as it builds on existing graph and distillation methods.
The paper tackles the semantic mismatch problem in statutory article retrieval by introducing QABISAR, a framework that uses bipartite interactions and knowledge distillation, achieving effectiveness on a real-world expert-annotated dataset.
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.