ECALP
Empirical Computational Argumentation in Legal Proceedings
Funded by the DFG
In this interdisciplinary collaboration, we look into argumentation in the verdicts of the European Court of Human Rights. What makes a verdict of a high importance? Is it the facts? Is it the argumentation pattern? Is it the judges? Or is it something left between the lines?
We combine legal expertise with state-of-the-art NLP.
We collaborate with expert legal researchers Prof. Dr. Christoph Burchard from Geothe University Frankfurt.
Prof. Dr. Christoph Burchard, LL.M. (NYU)
Goethe Universität Frankfurt am Main
Chair for German, European and International Criminal Law and Procedure, Comparative Law and Legal Theory
PrivaLingo
Truly Privacy-Preserving Machine Translation
Funded by the HMdIS
What does is mean for machine translation models to protect privacy? What personal information do neural machine translation systems leak? Can we protect users during inference?
In this research project supported by the Hessisches Ministerium des Innern und für Sport we tackle privacy-preserving natural language processing in the context of machine translation, including differential privacy and cryptographical tools.
ATHENE SenPai Text
Protecting Privacy and Sensitive Information in Texts
Funded by ATHENE
The goal of this project is to explore Natural Language Processing methods that can dynamically identify and obfuscate sensitive information in texts, with a focus on implicit attributes, for example, their ethnic background, income range, or personality traits. These methods will help to preserve the privacy of all individuals – both authors and other persons mentioned in the text. Further, we go beyond specific text sources, like social media, and aim to develop robust and highly adaptable methods that can generalize across domains and registers.
We collaborate with the UKP Lab led by Prof. Dr. Iryna Gurevych.
Prof. Dr. Iryna Gurevych
Technische Universität Darmstadt
Director of the Ubiquitous Knowledge Processing (UKP) Lab