Resumen: In this talk, I’ll focus on some recent advances in privacy-preserving NLP. In particular, we will look at the differential privacy paradigm and its applications in NLP, namely by using differentially-private training of neural networks. Although the training framework is very general, does it really fit everything we typically do in NLP?
Ponente: Dr. Habernal is leading an independent research group «Trustworthy Human Language Technologies» at the Department of Computer Science, Technical University of Darmstadt, Germany. His current research areas include privacy-preserving NLP, legal argument mining, and explainable and trustworthy models. His research track spans argument mining and computational argumentation, crowdsourcing, or serious games, among others. More info at www.trusthlt.org.