10 citations · 25 across the 5 of their papers we have counts for
7 papers
RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use
Pieter Delobelle, Thomas Winters, Bettina Berendt
Large transformer-based language models, e.g. BERT and GPT-3, outperform previous architectures on most natural language processing tasks. Such language models are first pre-traine…
RobBERTje: a Distilled Dutch BERT Model
Pieter Delobelle, Thomas Winters, Bettina Berendt
Pre-trained large-scale language models such as BERT have gained a lot of attention thanks to their outstanding performance on a wide range of natural language tasks. However, due…
Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
Kristen Scott, Pieter Delobelle, Bettina Berendt
We classify seven months' worth of Belgian COVID-related Tweets using multilingual BERT and relate them to their governments' COVID measures. We classify Tweets by their stated opi…
Dutch Humor Detection by Generating Negative Examples
Thomas Winters, Pieter Delobelle
Detecting if a text is humorous is a hard task to do computationally, as it usually requires linguistic and common sense insights. In machine learning, humor detection is usually m…
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Pieter Delobelle, Paul Temple, Gilles Perrouin +3
Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming u…
RobBERT: a Dutch RoBERTa-based Language Model
Pieter Delobelle, Thomas Winters, Bettina Berendt
Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural…