126 citations · 613 across the 33 of their papers we have counts for
7 papers · 1 filter
LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights
Lena Held, Ivan Habernal
Why does an argument end up in the final court decision? Was it deliberated or questioned during the oral hearings? Was there something in the hearings that triggered a particular…
DP-NMT: Scalable Differentially-Private Machine Translation
Timour Igamberdiev, Doan Nam Long Vu, Felix Künnecke +3
Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite sig…
To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?
Christopher Weiss, Frauke Kreuter, Ivan Habernal
Although the NLP community has adopted central differential privacy as a go-to framework for privacy-preserving model training or data sharing, the choice and interpretation of the…
Trade-Offs Between Fairness and Privacy in Language Modeling
Cleo Matzken, Steffen Eger, Ivan Habernal
Protecting privacy in contemporary NLP models is gaining in importance. So does the need to mitigate social biases of such models. But can we have both at the same time? Existing r…
Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
Nina Mouhammad, Johannes Daxenberger, Benjamin Schiller +1
Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be…
DP-BART for Privatized Text Rewriting under Local Differential Privacy
Timour Igamberdiev, Ivan Habernal
Privatized text rewriting with local differential privacy (LDP) is a recent approach that enables sharing of sensitive textual documents while formally guaranteeing privacy protect…
Differentially Private Natural Language Models: Recent Advances and Future Directions
Lijie Hu, Ivan Habernal, Lei Shen +1
Recent developments in deep learning have led to great success in various natural language processing (NLP) tasks. However, these applications may involve data that contain sensiti…