7 citations · 9 across the 7 of their papers we have counts for
4 papers · 1 filter
Do Language Models Learn about Legal Entity Types during Pretraining?
Claire Barale, Michael Rovatsos, Nehal Bhuta
Language Models (LMs) have proven their ability to acquire diverse linguistic knowledge during the pretraining phase, potentially serving as a valuable source of incidental supervi…
Empowering Refugee Claimants and their Lawyers: Using Machine Learning to Examine Decision-Making in Refugee Law
Claire Barale
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…
Refugee status determination: how cooperation with machine learning tools can lead to more justice
Claire Barale
Previous research on refugee status adjudications has shown that prediction of the outcome of an application can be derived from very few features with satisfactory accuracy. Recen…
Automated Refugee Case Analysis: An NLP Pipeline for Supporting Legal Practitioners
Claire Barale, Michael Rovatsos, Nehal Bhuta
In this paper, we introduce an end-to-end pipeline for retrieving, processing, and extracting targeted information from legal cases. We investigate an under-studied legal domain wi…