Distributional word vectors have recently been shown to encode many of the
human biases, most notably gender and racial biases, and models for attenuating
such biases have consequently been proposed. However, existing models and
studies (1) operate on under-specified and mutually differing bias definitions,
(2) are tailored for a particular bias (e.g., gender bias) and (3) have been
evaluated inconsistently and non-rigorously. In this work, we introduce a
general framework for debiasing word embeddings. We operationalize the
definition of a bias by discerning two types of bias specification: explicit
and implicit. We then propose three debiasing models that operate on explicit
or implicit bias specifications and that can be composed towards more robust
debiasing. Finally, we devise a full-fledged evaluation framework in which we
couple existing bias metrics with newly proposed ones. Experimental findings
across three embedding methods suggest that the proposed debiasing models are
robust and widely applicable: they often completely remove the bias both
implicitly and explicitly without degradation of semantic information encoded
in any of the input distributional spaces. Moreover, we successfully transfer
debiasing models, by means of cross-lingual embedding spaces, and remove or
attenuate biases in distributional word vector spaces of languages that lack
readily available bias specifications.

本论文提出了一个通用的去除词向量中性别、种族等偏见的方法，并通过新的评估指标验证了该框架的有效性，同时在跨语言文本中成功实现了去偏。