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    <abstract>Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact. In high-stakes decision making areas such as law, experts often require interpretability for automatic systems to be utilized in practical settings. In this work, we attempt to address these requirements applied to the important problem of legal citation prediction (LCP). We design the task with parallels to the thought-process of lawyers, i.e., with reference to both precedents and legislative provisions. After initial experimental results, we refine the target citation predictions with the feedback of legal experts. Additionally, we introduce a prototype architecture to add interpretability, achieving strong performance while adhering to decision parameters used by lawyers. Our study builds on and leverages the state-of-the-art language processing models for law, while addressing vital considerations for high-stakes tasks with practical societal impact.</abstract>
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%0 Conference Proceedings
%T Prototype-Based Interpretability for Legal Citation Prediction
%A Luo, Chu Fei
%A Bhambhoria, Rohan
%A Dahan, Samuel
%A Zhu, Xiaodan
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F luo-etal-2023-prototype
%X Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact. In high-stakes decision making areas such as law, experts often require interpretability for automatic systems to be utilized in practical settings. In this work, we attempt to address these requirements applied to the important problem of legal citation prediction (LCP). We design the task with parallels to the thought-process of lawyers, i.e., with reference to both precedents and legislative provisions. After initial experimental results, we refine the target citation predictions with the feedback of legal experts. Additionally, we introduce a prototype architecture to add interpretability, achieving strong performance while adhering to decision parameters used by lawyers. Our study builds on and leverages the state-of-the-art language processing models for law, while addressing vital considerations for high-stakes tasks with practical societal impact.
%R 10.18653/v1/2023.findings-acl.301
%U https://aclanthology.org/2023.findings-acl.301
%U https://doi.org/10.18653/v1/2023.findings-acl.301
%P 4883-4898

Markdown (Informal)

[Prototype-Based Interpretability for Legal Citation Prediction](https://aclanthology.org/2023.findings-acl.301) (Luo et al., Findings 2023)

• Prototype-Based Interpretability for Legal Citation Prediction (Luo et al., Findings 2023)

ACL

• Chu Fei Luo, Rohan Bhambhoria, Samuel Dahan, and Xiaodan Zhu. 2023. Prototype-Based Interpretability for Legal Citation Prediction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4883–4898, Toronto, Canada. Association for Computational Linguistics.