QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval

TL;DR

QABISAR is a new framework for statutory article retrieval that uses bipartite interactions and knowledge distillation to better capture query-article semantics, improving retrieval accuracy.

Contribution

It introduces bipartite query-article interactions and knowledge distillation into retrieval models to address semantic mismatch issues.

Findings

Effective on real-world expert-annotated dataset

Outperforms baseline methods in retrieval tasks

Captures multi-faceted query-article semantics

Abstract

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.