Our project aims at helping and supporting stakeholders in refugee status adjudications, such as lawyers, judges, governing bodies, and claimants, in order to make better decisions through data-driven intelligence and increase the understanding and transparency of the refugee application process for all involved parties. This PhD project has two primary objectives: (1) to retrieve past cases, and (2) to analyze legal decision-making processes on a dataset of Canadian cases. In this paper, we present the current state of our work, which includes a completed experiment on part (1) and ongoing efforts related to part (2). We believe that NLP-based solutions are well-suited to address these challenges, and we investigate the feasibility of automating all steps involved. In addition, we introduce a novel benchmark for future NLP research in refugee law. Our methodology aims to be inclusive to all end-users and stakeholders, with expected benefits including reduced time-to-decision, fairer and more transparent outcomes, and improved decision quality.

翻译：本项目旨在通过数据驱动的智能分析，帮助并支持难民身份裁定中的利益相关方（如律师、法官、管理机构及申请人）做出更优决策，同时提升各方对难民申请流程的理解与透明度。本博士研究有两个主要目标：(1) 检索过往案例；(2) 基于加拿大案例数据集分析法律决策过程。本文呈现了现阶段工作进展，包括针对目标(1)的已完成实验，以及目标(2)的相关持续研究。我们认为基于NLP的解决方案非常适合应对这些挑战，并探究了自动化全部步骤的可行性。此外，我们为未来难民法律领域的NLP研究引入了新的基准测试。本研究方法旨在覆盖所有终端用户与利益相关者，预期成果包括缩短决策时间、实现更公平透明的结果，以及提升决策质量。