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智能，帮助和支持难民身份裁定中的利益相关者（如律师、法官、管理机构及申请人）做出更优决策，并增进各方对难民申请流程的理解与透明度。该博士项目包含两大核心目标：（1）检索历史案例，以及（2）基于加拿大案例数据集分析法律决策过程。本文展示了当前工作进展，包括针对目标（1）已完成实验的成果，以及目标（2）的持续推进工作。我们深信基于自然语言处理的解决方案能够有效应对这些挑战，并探讨了全流程自动化的可行性。此外，我们为难民法律领域的未来自然语言处理研究提出了新的基准。本方法论力求惠及所有终端用户与利益相关者，预期效益包括缩短决策周期、实现更公正透明的结果，以及提升决策质量。