Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction. Current approaches show promising progress towards debiasing single bias dimensions such as gender or race. In this paper, we present a joint multiclass debiasing approach that is capable of debiasing multiple bias dimensions simultaneously. In that direction, we present two approaches, HardWEAT and SoftWEAT, that aim to reduce biases by minimizing the scores of the Word Embeddings Association Test (WEAT). We demonstrate the viability of our methods by debiasing Word Embeddings on three classes of biases (religion, gender and race) in three different publicly available word embeddings and show that our concepts can both reduce or even completely eliminate bias, while maintaining meaningful relationships between vectors in word embeddings. Our work strengthens the foundation for more unbiased neural representations of textual data.

中文翻译：

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词嵌入的联合多类去偏差

Word Embeddings 中的偏差以及减少偏差的努力一直是最近引起关注的主题。当前的方法在消除单一偏见维度（如性别或种族）方面取得了可喜的进展。在本文中，我们提出了一种联合多类去偏差方法，能够同时对多个偏差维度进行去偏差。在这个方向上，我们提出了两种方法，HardWE​​AT 和 SoftWEAT，旨在通过最小化 Word Embeddings Association Test (WEAT) 的分数来减少偏差。我们通过在三种不同的公开可用的词嵌入中对三类偏见（宗教、性别和种族）的词嵌入进行去偏见来证明我们方法的可行性，并表明我们的概念既可以减少甚至完全消除偏见，同时保持向量之间有意义的关系在词嵌入中。