QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval
Santosh T.y.s.s, Hassan Sarwat, Matthias Grabmair
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.
BibTeX
@inproceedings{t-y-s-s-etal-2025-qabisar,
title = "{QABISAR}: Query-Article Bipartite Interactions for Statutory Article Retrieval",
author = "T.y.s.s, Santosh and
Sarwat, Hassan and
Grabmair, Matthias",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.100/",
pages = "1496--1502"
}