arXiv cs.AI· Zarah Shibli, Areej Alhothali, Lulwah Alharigy·· 14 小时前AI 评分26
从阿拉伯语司法叙事中检索法条：沙特阿拉伯家庭法与证据法基准
Statutory Article Retrieval from Arabic Judicial Narratives: Family Law and Law of Evidence in Saudi Arabia
AI 导读
研究构建了包含200个合成沙特阿拉伯语司法案例与381条家庭法、证据法条文的基准，用于检验叙事到法律规则的检索。指令版 Qwen3-Embedding-8B 稠密编码器在全语料上取得 MRR@10 0.849，但仅限证据法条文时骤降至 0.087，与 BM25 融合后也仅 0.207。
正文 · 原文
Abstract:Reliable legal information retrieval must ground legal reasoning in authoritative statutory sources. In Arabic judicial settings, long factual narratives must be mapped to concise articles that express abstract legal rules, creating a narrative-to-legal-rule abstraction gap. Using a benchmark of 200 synthetic Saudi Arabic judicial cases and 381 articles from the Family Law and the Law of Evidence, we compare word-level and char-4 BM25, zero-shot dense encoders, per-law retrieval, dense-sparse reciprocal rank fusion (RRF), and a proposed Decompose-and-Refine (DaR) pipeline. In DaR, a large language model splits each narrative into disputed legal issues and generates, for each issue, three retrieval channels: a statute-style paraphrase, legal keywords, and a candidate taxonomy chapter. On the whole corpus, the instructed Qwen3-Embedding-8B dense encoder reaches an MRR@10 of 0.849, the best result of any non-decomposed system. Under per-law scoring, this advantage reflects Family Law only: restricted to Evidence articles, the same encoder drops to an MRR@10 of 0.087, and fusion with BM25 raises it only to 0.207. DaR changes this picture, raising Evidence MRR@10 to 0.696 and Family MRR@10 to 0.864. Without decomposition, the two laws favor different strategies; under DaR, the same retriever achieves the best MRR@10 on both.
| Comments: | Submitted to the MLNLP 2026 Conference |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.38336 [cs.CL] |
| (or arXiv:2610.38336v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38336 arXiv-issued DOI via DataCite |
Submission history
From: Zarah Shibli [view email]
[v1]
Wed, 28 Oct 2026 06:36:24 UTC (2,117 KB)
来源：arXiv cs.AI · arxiv.org