  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.
