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Found 13 papers, 7 papers with code
Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models
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.
Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptation
This one-to-many token mapping improves tremendously the initialization of the embedding table for the target language.
How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification
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.
RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use
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.
FairDistillation: Mitigating Stereotyping in Language Models
Large pre-trained language models are successfully being used in a variety of tasks, across many languages.
RobBERTje: a Distilled Dutch BERT Model
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.
Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models
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.
Measuring Shifts in Attitudes Towards COVID-19 Measures 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.
Dutch Humor Detection by Generating Negative Examples
Detecting if a text is humorous is a hard task to do computationally, as it usually requires linguistic and common sense insights.
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
These new examples are then used to retrain and improve the model in the first step.
RobBERT: a Dutch RoBERTa-based Language Model
Training a Dutch BERT model thus has a lot of potential for a wide range of Dutch NLP tasks.
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Time to Take Emoji Seriously: They Vastly Improve Casual Conversational Models
Graphical emoji are ubiquitous in modern-day online conversations.
Computational Ad Hominem Detection
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.
