  Recent research in Natural Language Processing has revealed that word
embeddings can encode social biases present in the training data which can
affect minorities in real world applications. This paper explores the gender
bias implicit in Dutch embeddings while investigating whether English language
based approaches can also be used in Dutch. We implement the Word Embeddings
Association Test (WEAT), Clustering and Sentence Embeddings Association Test
(SEAT) methods to quantify the gender bias in Dutch word embeddings, then we
proceed to reduce the bias with Hard-Debias and Sent-Debias mitigation methods
and finally we evaluate the performance of the debiased embeddings in
downstream tasks. The results suggest that, among others, gender bias is
present in traditional and contextualized Dutch word embeddings. We highlight
how techniques used to measure and reduce bias created for English can be used
in Dutch embeddings by adequately translating the data and taking into account
the unique characteristics of the language. Furthermore, we analyze the effect
of the debiasing techniques on downstream tasks which show a negligible impact
on traditional embeddings and a 2% decrease in performance in contextualized
embeddings. Finally, we release the translated Dutch datasets to the public
along with the traditional embeddings with mitigated bias.
