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.

本研究使用 Word Embeddings Association Test (WEAT)、Clustering 和 Sentence Embeddings Association Test (SEAT) 等方法，衡量荷兰语词嵌入中的性别偏见，并使用 Hard-Debias 和 Sent-Debias 调控方法，探索性别偏见对下游任务的影响。结果表明，传统和上下文嵌入中存在性别偏见，研究人员提供了翻译荷兰语数据集和减轻偏误的嵌入。

荷兰语词嵌入的偏差评估