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    <abstract>Legal systems often employ legislative drafting practices in which higher-level laws delegate detailed regulations to lower-level laws. In particular, when a single provision delegates authority to multiple lower-level laws, readers must cross-reference many related provisions scattered across the entire bodies of law, imposing a substantial cognitive burden. Reflecting this real-world problem, this work proposes a novel multi-label delegation target identification task to identify all the delegated provisions corresponding to an input provision, with the focus on Japanese law. We construct pipeline systems combining a candidate retriever and a candidate classifier, using language models with diverse architectures. Experimental results show that adopting a cross-encoder-based binary classifier as a candidate classifier improves performance at finer granularities, establishing a solid baseline for the task. Furthermore, our comprehensive analyses reveal the remaining challenges in achieving sufficient performance on instances with many delegated provisions and identify typical error types, providing a foundation for future improvement.</abstract>
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%0 Conference Proceedings
%T Multi-Label Delegation Target Identification in Japanese Law
%A Fujita, Tsuyoshi
%A Sawada, Yuya
%A Sakai, Yusuke
%A Watanabe, Taro
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F fujita-etal-2026-multi
%X Legal systems often employ legislative drafting practices in which higher-level laws delegate detailed regulations to lower-level laws. In particular, when a single provision delegates authority to multiple lower-level laws, readers must cross-reference many related provisions scattered across the entire bodies of law, imposing a substantial cognitive burden. Reflecting this real-world problem, this work proposes a novel multi-label delegation target identification task to identify all the delegated provisions corresponding to an input provision, with the focus on Japanese law. We construct pipeline systems combining a candidate retriever and a candidate classifier, using language models with diverse architectures. Experimental results show that adopting a cross-encoder-based binary classifier as a candidate classifier improves performance at finer granularities, establishing a solid baseline for the task. Furthermore, our comprehensive analyses reveal the remaining challenges in achieving sufficient performance on instances with many delegated provisions and identify typical error types, providing a foundation for future improvement.
%U https://aclanthology.org/2026.findings-emnlp.2225/
%P 47655-47682

Markdown (Informal)

[Multi-Label Delegation Target Identification in Japanese Law](https://aclanthology.org/2026.findings-emnlp.2225/) (Fujita et al., Findings 2026)

• Multi-Label Delegation Target Identification in Japanese Law (Fujita et al., Findings 2026)

ACL

• Tsuyoshi Fujita, Yuya Sawada, Yusuke Sakai, and Taro Watanabe. 2026. Multi-Label Delegation Target Identification in Japanese Law. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47655–47682, Budapest, Hungary. Association for Computational Linguistics.