75 citations · 129 across the 26 of their papers we have counts for
6 papers · 2 filters
Privacy-Preserving Models for Legal Natural Language Processing
Ying Yin, Ivan Habernal
Pre-training large transformer models with in-domain data improves domain adaptation and helps gain performance on the domain-specific downstream tasks. However, sharing models pre…
The Legal Argument Reasoning Task in Civil Procedure
Leonard Bongard, Lena Held, Ivan Habernal
We present a new NLP task and dataset from the domain of the U.S. civil procedure. Each instance of the dataset consists of a general introduction to the case, a particular questio…
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application
Ramit Sawhney, Atula Tejaswi Neerkaje, Ivan Habernal +1
Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance…
DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting
Timour Igamberdiev, Thomas Arnold, Ivan Habernal
Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems…
Mining Legal Arguments in Court Decisions
Ivan Habernal, Daniel Faber, Nicola Recchia +4
Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been…
How reparametrization trick broke differentially-private text representation learning
Ivan Habernal
As privacy gains traction in the NLP community, researchers have started adopting various approaches to privacy-preserving methods. One of the favorite privacy frameworks, differen…