no code implementations • 8 Dec 2023 • Lena Held, Ivan Habernal
Was there something in the hearings that triggered a particular judge to write a dissenting opinion?
1 code implementation • 24 Nov 2023 • Timour Igamberdiev, Doan Nam Long Vu, Felix Künnecke, Zhuo Yu, Jannik Holmer, Ivan Habernal
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 significant data privacy concerns for NMT systems.
no code implementations • 13 Jul 2023 • 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 key parameter, privacy budget $\varepsilon$ that governs the strength of privacy protection, remains largely arbitrary.
1 code implementation • 24 May 2023 • Cleo Matzken, Steffen Eger, Ivan Habernal
Protecting privacy in contemporary NLP models is gaining in importance.
no code implementations • 6 Mar 2023 • Nina Mouhammad, Johannes Daxenberger, Benjamin Schiller, Ivan Habernal
Most tasks in NLP require labeled data.
1 code implementation • 15 Feb 2023 • 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 protection to individuals.
no code implementations • 22 Jan 2023 • Lijie Hu, Ivan Habernal, Lei Shen, Di Wang
In this paper, we provide the first systematic review of recent advances in DP deep learning models in NLP.
1 code implementation • 5 Nov 2022 • Leonard Bongard, Lena Held, Ivan Habernal
We present a new NLP task and dataset from the domain of the U. S. civil procedure.
1 code implementation • 5 Nov 2022 • 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.
no code implementations • 5 Sep 2022 • Ramit Sawhney, Atula Tejaswi Neerkaje, Ivan Habernal, Lucie Flek
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 of guaranteeing privacy of user data.
1 code implementation • COLING 2022 • 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.
1 code implementation • 12 Aug 2022 • Ivan Habernal, Daniel Faber, Nicola Recchia, Sebastian Bretthauer, Iryna Gurevych, Indra Spiecker genannt Döhmann, Christoph Burchard
Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field.
1 code implementation • ACL 2022 • Ivan Habernal
As privacy gains traction in the NLP community, researchers have started adopting various approaches to privacy-preserving methods.
1 code implementation • 15 Dec 2021 • Manuel Senge, Timour Igamberdiev, Ivan Habernal
Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price.
2 code implementations • EMNLP 2021 • Ivan Habernal
Our proof reveals that ADePT is not differentially private, thus rendering the experimental results unsubstantiated.
1 code implementation • LREC 2022 • Timour Igamberdiev, Ivan Habernal
Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e. g., citation or social networks.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Max Glockner, Ivan Habernal, Iryna Gurevych
We propose a differentiable training-framework to create models which output faithful rationales on a sentence level, by solely applying supervision on the target task.
no code implementations • SEMEVAL 2018 • Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, Benno Stein
A natural language argument is composed of a claim as well as reasons given as premises for the claim.
no code implementations • WS 2018 • Ivan Habernal
The classical view on argumentation, such that arguments are logical structures consisting of different distinguishable parts and that parties exchange arguments in a rational way, is prevalent in textbooks but nonexistent in the real world.
1 code implementation • NAACL 2018 • Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, Benno Stein
Arguing without committing a fallacy is one of the main requirements of an ideal debate.
3 code implementations • NAACL 2018 • Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, Benno Stein
On this basis, we present a new challenging task, the argument reasoning comprehension task.
1 code implementation • EMNLP 2017 • Ivan Habernal, Raffael Hannemann, Christian Pollak, Christopher Klamm, Patrick Pauli, Iryna Gurevych
An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies.
no code implementations • ACL 2017 • Henning Wachsmuth, Nona Naderi, Ivan Habernal, Yufang Hou, Graeme Hirst, Iryna Gurevych, Benno Stein
Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches.
1 code implementation • EMNLP 2017 • Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, Iryna Gurevych
Argument mining has become a popular research area in NLP.
no code implementations • LREC 2016 • Maria Sukhareva, Judith Eckle-Kohler, Ivan Habernal, Iryna Gurevych
We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing.
1 code implementation • LREC 2016 • Ivan Habernal, Omnia Zayed, Iryna Gurevych
Large Web corpora containing full documents with permissive licenses are crucial for many NLP tasks.
no code implementations • CL 2017 • Ivan Habernal, Iryna Gurevych
The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation.