1 citations · 2 across the 5 of their papers we have counts for
7 papers
TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models
Ali Davody, David Ifeoluwa Adelani, Thomas Kleinbauer +1
Transferring knowledge from one domain to another is of practical importance for many tasks in natural language processing, especially when the amount of available data in the targ…
Exploiting Social Media Content for Self-Supervised Style Transfer
Dana Ruiter, Thomas Kleinbauer, Cristina España-Bonet +2
Recent research on style transfer takes inspiration from unsupervised neural machine translation (UNMT), learning from large amounts of non-parallel data by exploiting cycle consis…
Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online
Dana Ruiter, Liane Reiners, Ashwin Geet D'Sa +6
Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label…
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
David Ifeoluwa Adelani, Miaoran Zhang, Xiaoyu Shen +3
Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g. their gender or ethnicity. Style transfer is an effective…
Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces
Vanessa Hahn, Dana Ruiter, Thomas Kleinbauer +1
Hate speech and profanity detection suffer from data sparsity, especially for languages other than English, due to the subjective nature of the tasks and the resulting annotation i…
Privacy Guarantees for De-identifying Text Transformations
David Ifeoluwa Adelani, Ali Davody, Thomas Kleinbauer +1
Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data. At the same time, there is a clear need for protect…
On the effect of normalization layers on Differentially Private training of deep Neural networks
Ali Davody, David Ifeoluwa Adelani, Thomas Kleinbauer +1
Differentially private stochastic gradient descent (DPSGD) is a variation of stochastic gradient descent based on the Differential Privacy (DP) paradigm, which can mitigate privacy…