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.

我们的项目旨在通过数据驱动的智能帮助和支持难民地位裁决的利益相关者，例如律师、法官、治理机构和申请人，从而使所有相关方做出更好的决策，并增加对难民申请过程的理解和透明度。我们的目标有两个：检索过去的案例和分析加拿大案例数据上的法律决策过程。本文介绍了我们工作的当前状态，包括已完成的第一部分实验和与第二部分相关的正在进行的工作。我们相信以自然语言处理为基础的解决方案非常适合应对这些挑战，因此我们调查了自动化所有涉及步骤的可行性。此外，我们引入了一个未来自然语言处理研究中难民法领域的新基准。我们的方法旨在包容所有最终用户和利益相关者，预期收益包括缩短决策时间、更公平和透明的结果以及提高决策质量。

赋能难民申请人及其律师: 利用机器学习研究难民法决策