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    <abstract>Legal professionals spend considerable time identifying statutes relevant to legal cases by navigating lengthy and hierarchically structured legal documents. While automated statute retrieval has advanced for high-resource languages to resolve the problem, the Nepali legal domain still lacks a standardized benchmark for realistic case-to-statute retrieval and systematic evaluation. To address this gap, we introduce NELSR (Nepali Legal Statute Retrieval Benchmark), comprising 5,731 case-derived queries extracted from Supreme Court judgments and a corpus of 302 federal statutes. NELSR is constructed using a citation-aware query construction approach that establishes reliable query–statute relevance pairs and legal structure-aware segmentation to preserve the semantic and hierarchical organization of statutory units. We establish retrieval baselines by evaluating sparse, dense, and hybrid retrieval methods and investigate the contributions of lexical and semantic retrieval signals through ablation studies. Experimental results show that hybrid retrieval achieves the best performance, with a Recall@100 of 0.66, nDCG@10 of 0.34, and MRR@10 of 0.32.</abstract>
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%0 Conference Proceedings
%T NELSR: A Nepali Legal Benchmark for Case-to-Statute Retrieval
%A Parajuli, Anjan
%A Kunwar, Darshan
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F parajuli-kunwar-2026-nelsr
%X Legal professionals spend considerable time identifying statutes relevant to legal cases by navigating lengthy and hierarchically structured legal documents. While automated statute retrieval has advanced for high-resource languages to resolve the problem, the Nepali legal domain still lacks a standardized benchmark for realistic case-to-statute retrieval and systematic evaluation. To address this gap, we introduce NELSR (Nepali Legal Statute Retrieval Benchmark), comprising 5,731 case-derived queries extracted from Supreme Court judgments and a corpus of 302 federal statutes. NELSR is constructed using a citation-aware query construction approach that establishes reliable query–statute relevance pairs and legal structure-aware segmentation to preserve the semantic and hierarchical organization of statutory units. We establish retrieval baselines by evaluating sparse, dense, and hybrid retrieval methods and investigate the contributions of lexical and semantic retrieval signals through ablation studies. Experimental results show that hybrid retrieval achieves the best performance, with a Recall@100 of 0.66, nDCG@10 of 0.34, and MRR@10 of 0.32.
%U https://aclanthology.org/2026.nllp-1.43/
%P 524-534

Markdown (Informal)

[NELSR: A Nepali Legal Benchmark for Case-to-Statute Retrieval](https://aclanthology.org/2026.nllp-1.43/) (Parajuli & Kunwar, NLLP 2026)

• NELSR: A Nepali Legal Benchmark for Case-to-Statute Retrieval (Parajuli & Kunwar, NLLP 2026)

ACL

• Anjan Parajuli and Darshan Kunwar. 2026. NELSR: A Nepali Legal Benchmark for Case-to-Statute Retrieval. In Proceedings of the Natural Legal Language Processing Workshop 2026, pages 524–534, Budapest, Hungary. Association for Computational Linguistics.