1 citations · 2 across the 8 of their papers we have counts for
10 papers
Improving and Evaluating the Detection of Fragmentation in News Recommendations with the Clustering of News Story Chains
Alessandra Polimeno, Myrthe Reuver, Sanne Vrijenhoek +1
News recommender systems play an increasingly influential role in shaping information access within democratic societies. However, tailoring recommendations to users' specific inte…
Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection
Pantea Haghighatkhah, Antske Fokkens, Pia Sommerauer +2
Bias elimination and recent probing studies attempt to remove specific information from embedding spaces. Here it is important to remove as much of the target information as possib…
Dealing with Abbreviations in the Slovenian Biographical Lexicon
Angel Daza, Antske Fokkens, Tomaž Erjavec
Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. They can also make the text less readable, especially in…
Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognition
Jonathan Kamp, Lisa Beinborn, Antske Fokkens
Argument Unit Recognition and Classification aims at identifying argument units from text and classifying them as pro or against. One of the design choices that need to be made whe…
Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions
Urja Khurana, Ivar Vermeulen, Eric Nalisnick +2
\textbf{Offensive Content Warning}: This paper contains offensive language only for providing examples that clarify this research and do not reflect the authors' opinions. Please b…
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task
Urja Khurana, Eric Nalisnick, Antske Fokkens
Despite their success, modern language models are fragile. Even small changes in their training pipeline can lead to unexpected results. We study this phenomenon by examining the r…