# Empowering Refugee Claimants and their Lawyers: Using Machine Learning to Examine Decision-Making in Refugee Law

arXiv: https://arxiv.org/abs/2308.11531 · HTML: https://mlsanity.com/paper/2308.11531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Large-Scale Multi-Label Text Classification on EU Legislation](https://mlsanity.com/paper/1906.02192/) (2019)
- [FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing](https://mlsanity.com/paper/2203.07228/) (2022)
- [EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://mlsanity.com/paper/2210.13448/) (2022)
- [LexNLP: Natural language processing and information extraction for legal and regulatory texts](https://mlsanity.com/paper/1806.03688/) (2018)
- [Extreme Multi-Label Legal Text Classification: A case study in EU Legislation](https://mlsanity.com/paper/1905.10892/) (2019)
- [ferret: a Framework for Benchmarking Explainers on Transformers](https://mlsanity.com/paper/2208.01575/) (2022)
- [ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining](https://mlsanity.com/paper/2209.01650/) (2022)
- [Mittens: An Extension of GloVe for Learning Domain-Specialized Representations](https://mlsanity.com/paper/1803.09901/) (2018)

## Built on by (most cited first, 1 of 1)

- [ImmigrationReason: A Structured Dataset of U.S. Immigration Appeals for Legal Reasoning Research](https://mlsanity.com/paper/2608.20391/) (2026)

## Abstract (arXiv)

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.