no code implementations • NAACL 2022 • Pieter Delobelle, Ewoenam Tokpo, Toon Calders, Bettina Berendt
We survey the literature on fairness metrics for pre-trained language models and experimentally evaluate compatibility, including both biases in language models and in their downstream tasks.
no code implementations • 5 Oct 2023 • François Remy, Pieter Delobelle, Bettina Berendt, Kris Demuynck, Thomas Demeester
This one-to-many token mapping improves tremendously the initialization of the embedding table for the target language.
1 code implementation • 30 Jan 2023 • Ewoenam Tokpo, Pieter Delobelle, Bettina Berendt, Toon Calders
Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this.
no code implementations • 15 Nov 2022 • Pieter Delobelle, Thomas Winters, Bettina Berendt
To evaluate if our new model is a plug-in replacement for RobBERT, we introduce two additional criteria based on concept drift of existing tokens and alignment for novel tokens. We found that for certain language tasks this update results in a significant performance increase.
1 code implementation • 10 Jul 2022 • Pieter Delobelle, Bettina Berendt
Large pre-trained language models are successfully being used in a variety of tasks, across many languages.
no code implementations • 28 Apr 2022 • Pieter Delobelle, Thomas Winters, Bettina Berendt
We found that the performance of the models using the shuffled versus non-shuffled datasets is similar for most tasks and that randomly merging subsequent sentences in a corpus creates models that train faster and perform better on tasks with long sequences.
1 code implementation • 14 Dec 2021 • Pieter Delobelle, Ewoenam Kwaku Tokpo, Toon Calders, Bettina Berendt
We survey the existing literature on fairness metrics for pretrained language models and experimentally evaluate compatibility, including both biases in language models as in their downstream tasks.
1 code implementation • 20 Apr 2021 • 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.
no code implementations • 26 Oct 2020 • 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.
1 code implementation • 14 May 2020 • Pieter Delobelle, Paul Temple, Gilles Perrouin, Benoît Frénay, Patrick Heymans, Bettina Berendt
These new examples are then used to retrain and improve the model in the first step.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Pieter Delobelle, Thomas Winters, Bettina Berendt
Training a Dutch BERT model thus has a lot of potential for a wide range of Dutch NLP tasks.
Ranked #1 on Sentiment Analysis on DBRD
no code implementations • 30 Oct 2019 • Pieter Delobelle, Bettina Berendt
Graphical emoji are ubiquitous in modern-day online conversations.
1 code implementation • ACL 2019 • Pieter Delobelle, Murilo Cunha, Eric Massip Cano, Jeroen Peperkamp, Bettina Berendt
Fallacies like the personal attack{---}also known as the ad hominem attack{---}are introduced in debates as an easy win, even though they provide no rhetorical contribution.