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Found 34 papers, 20 papers with code
LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights
Was there something in the hearings that triggered a particular judge to write a dissenting opinion?
DP-NMT: Scalable Differentially-Private Machine Translation
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.
To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?
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.
Trade-Offs Between Fairness and Privacy in Language Modeling
Protecting privacy in contemporary NLP models is gaining in importance.
Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
Most tasks in NLP require labeled data.
DP-BART for Privatized Text Rewriting under Local Differential Privacy
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.
Differentially Private Natural Language Models: Recent Advances and Future Directions
In this paper, we provide the first systematic review of recent advances in DP deep learning models in NLP.
The Legal Argument Reasoning Task in Civil Procedure
We present a new NLP task and dataset from the domain of the U. S. civil procedure.
Privacy-Preserving Models for Legal Natural Language Processing
Pre-training large transformer models with in-domain data improves domain adaptation and helps gain performance on the domain-specific downstream tasks.
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application
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.
DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting
Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents.
Mining Legal Arguments in Court Decisions
Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field.
How reparametrization trick broke differentially-private text representation learning
As privacy gains traction in the NLP community, researchers have started adopting various approaches to privacy-preserving methods.
One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks
Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price.
When differential privacy meets NLP: The devil is in the detail
Our proof reveals that ADePT is not differentially private, thus rendering the experimental results unsubstantiated.
Privacy-Preserving Graph Convolutional Networks for Text Classification
Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e. g., citation or social networks.
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision
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.
Computational Argumentation: A Journey Beyond Semantics, Logic, Opinions, and Easy Tasks
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.
SemEval-2018 Task 12: The Argument Reasoning Comprehension Task
A natural language argument is composed of a claim as well as reasons given as premises for the claim.
Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation
Arguing without committing a fallacy is one of the main requirements of an ideal debate.
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
On this basis, we present a new challenging task, the argument reasoning comprehension task.
Argotario: Computational Argumentation Meets Serious Games
An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies.
Argumentation Quality Assessment: Theory vs. Practice
Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches.
What is the Essence of a Claim? Cross-Domain Claim Identification
Argument mining has become a popular research area in NLP.
C4Corpus: Multilingual Web-size Corpus with Free License
Large Web corpora containing full documents with permissive licenses are crucial for many NLP tasks.
Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations
We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing.
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation.
