构建跨法域合同条款等效性数据集，助力跨国法律文本理解
LAUKIN: A Multi-jurisdictional Common Law Contract Dataset
- 基于多阶段检索与重排序构建条款配对，专家标注3000对等效性
- 包含14727对条款，最佳模型宏F1达65.11%，挑战显著
- 适用于法律NLP、跨法域合同分析及半监督学习研究
跨国公司日益需要跨法域合同审查，但现有法律NLP数据集多局限于单一法域。本文提出LAUKIN（澳大利亚、英国、印度法律等效性数据集），包含澳-英、英-印、印-澳三组条款对，标注布尔等效性。通过多阶段检索与重排序管道构建初始配对，再由法律专家标注3000对（900训练、600验证、1500测试）。数据集涵盖204份合同、8类协议，共14727对条款。评估12种模型，最优宏F1为65.11%，表明尽管共享法律传统，但起草惯例差异大，跨法域等效性判断复杂。此外，11727对未标注数据支持未来半监督学习研究。
原文摘要 · Abstract (English)
Multinational companies increasingly require cross-jurisdictional contract review, yet existing legal NLP datasets are largely restricted to a single jurisdiction. We introduce LAUKIN (Legal equivalence dataset of Australia, UK, and INdia), a dataset of clause pairs (AU-UK, UK-IN, IN-AU) labelled for boolean legal equivalence. We develop a novel multi-stage retrieval and reranking pipeline to construct the initial clause pair mapping, with a subset of clause pairs subsequently annotated by legal experts as Equivalent or Not Equivalent. The dataset comprises 14,727 clause pairs from 204 contracts across 8 agreement types, of which 3,000 are manually labelled: 900 train, 600 dev, and 1,500 test. We evaluate 12 models across 4 techniques, achieving a best macro-F1 of 65.11%, establishing LAUKIN as a challenging benchmark. Results reveal that, despite shared legal heritage, drafting conventions diverge significantly across jurisdictions, making cross-jurisdictional equivalence classification non-trivial. LAUKIN also includes 11,727 unlabelled training pairs to support future semi-supervised learning research in legal NLP.
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