Pieter Delobelle

Hi. I'm a PhD researcher on fairness and natural language processing at the DTAI lab of the KU Leuven under the supervision of Luc De Raedt and Bettina Berendt. You can find me on Twitter or send me an email.


June 07, 2023 I was interviewed about my research on fairness and RobBERT for De Groene Amsterdammer. June 01, 2023 I got an internship position at Apple's MLR team this summer. May 01, 2023 I was interviewed for Scoop Magazine. May 01, 2023 I am doing a research visit to Bocconi in Milan. April 19, 2023 Thomas Winters was interviewed in 'Knack' and mentioned RobBERT. April 24, 2023 Generative AI: Artificial intelligence as a creative partner. March 31, 2023 I was interviewed for the Flemish newspaper 'De Morgen'. February 10, 2023 I talked about language models for HR and PES in Ghent. January 20, 2023 Our paper on biases in language models got accepted at EACL '23. View older news
Posted on June 28, 2023


Improving Resume Quality Through Co-Creative Tools

Clear and well-written resumes can help jobseekers find better and better-suited jobs. However, many people struggle with writing their resumes, especially if they just entered the job market. Although many tools have been created to help write resumes, an analysis we conducted showed us that these tools focus mainly on layout and only give very limited content-related support. We present a co-creative resume building tool that provides tailored advice to jobseekers based on a comprehensive computational analysis of 444k resumes and the development of a Dutch language model, ResumeRobBERT, to provide contextual suggestions.

Posted on February 07, 2023

How far can it go?

On Intrinsic Gender Bias Mitigation for Text Classification

We have designed a probe to investigate the effects of intrinsic gender bias mitigation strategies on downstream text classification tasks. We find that instead of resolving gender bias, these strategies are able to hide it while retaining significant gender information in the embeddings. Based on these findings, we recommend that intrinsic bias mitigation techniques should be combined with other fairness interventions for downstream tasks.

Posted on November 15, 2022


Updating a Dutch Language Model to Account for Evolving Language Use

We update the RobBERT Dutch language model to include new high-frequent tokens present in the latest Dutch OSCAR corpus from 2022. We then pre-train the RobBERT model using this dataset. Our new model is a plug-in replacement for RobBERT and results in a significant performance increase for certain language tasks.

Posted on August 16, 2022


Mitigating Stereotyping in Language Models

Large pre-trained language models are successfully being used in a variety of tasks, across many languages. With this ever-increasing usage, the risk of harmful side eļ¬€ects also rises, for example by reproducing and reinforcing stereotypes. However, detecting and mitigating these harms is diļ¬ƒcult to do in general and becomes computationally expensive when tackling multiple languages or when considering diļ¬€erent biases. To address this, we present FairDistillation : a cross-lingual method based on knowledge distillation to construct smaller language models while controlling for speciļ¬c biases.

Posted on December 15, 2021

Measuring Fairness with Biased Rulers

A Survey on Quantifying Biases in Pretrained Language Models

An increasing awareness of biased patterns in natural language processing resources, like BERT, has motivated many metrics to quantify 'bias' and 'fairness'. But comparing the results of different metrics and the works that evaluate with such metrics remains difficult, if not outright impossible. 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. We do this by a mixture of traditional literature survey and correlation analysis, as well as by running empirical evaluations. We find that many metrics are not compatible and highly depend on templates, attribute and target seeds and the choice of embeddings.

Posted on April 21, 2021

Attitudes Towards COVID-19 Measures

Measuring Shifts in Belgium Using Multilingual BERT

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 opinion on Belgian government curfew measures (too strict, ok, too loose). We examine the change in topics discussed and views expressed over time and in reference to dates of related events such as implementation of new measures or COVID-19 related announcements in the media.

Posted on September 14, 2020

Ethical Adversaries

Towards Mitigating Unfairness with Adversarial Machine Learning

We offer a new framework that assists in mitigating unfair representations in the dataset used for training. Our framework relies on adversaries to improve fairness. First, it evaluates a model for unfairness w.r.t. protected attributes and ensures that an adversary cannot guess such attributes for a given outcome, by optimizing the modelā€™s parameters for fairness while limiting utility losses. Second, the framework leverages evasion attacks from adversarial machine learning to perform adversarial retraining with new examples unseen by the model. We evaluated our framework on well-studied datasets in the fairness literature where it can surpass other approaches concerning demographic parity, equality of opportunity and also the modelā€™s utility.

Posted on January 20, 2020


A Dutch RoBERTa-based Language Model

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 language tasks. One of the most prominent pre-trained language models is BERT. Although the multilingual version of BERT performs well on many tasks, recent studies showed that BERT models trained on a single language significantly outperform the multilingual results.<br/>For this reason we present a Dutch model based on RoBERTa, which we call RobBERT. We show that RobBERT improves state of the art results in Dutch-specific language tasks.

Posted on October 03, 2019

Time to Take Emoji Seriously

They Vastly Improve Casual Conversational Models

Graphical emoji are ubiquitous in modern-day online conversations. So is a single thumbs-up emoji able to signify an agreement, without any words. Current state-of-the-art systems are ill-equipped to correctly interpret these emoji, especially in a conversational context. However, in a casual context, the benefits might be high: a better understanding of usersā€™ utterances and more natural, emoji-rich responses.<br/>With this in mind, we modify BERT to fully support emoji, both from the Unicode Standard and custom emoji. This modified BERT is then trained on a corpus of question-answer (QA) tuples with a high number of emoji.

Posted on August 01, 2019

Computational Ad Hominem Detection

Fallacies like the personal attackā€”also known as the ad hominem attackā€”are introduced in debates as an easy win, even though they provide no rhetorical contribution. Although their importance in argumentation mining is acknowledged, automated mining and analysis is still lacking. We show TF-IDF approaches are insufficient to detect the ad hominem attack. Therefore we present a machine learning approach for information extraction, which has a recall of 80% for a social media data source. We also demonstrate our approach with an application that uses online learning.