汉字成分的无监督缓解性别偏见——以中文词嵌入为例

Xiuying Chen, Mingzhe Li, Rui Yan, Xin Gao, Xiangliang Zhang
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引用次数: 3

摘要

从大量文本集合中学习到的词嵌入已经显示出显著程度的歧视性偏见。然而,作为最常用的语言之一,对汉语的研究却很少。同时,现有文献依赖于人工创建补充数据,耗时耗力。在这项工作中,我们提出了第一个基于Word2vec的中文中性词嵌入模型(CGE),该模型在没有任何标记数据的情况下学习中性词嵌入。具体来说,CGE在训练过程中利用和强调了词根这一汉字成分中所包含的丰富的女性和男性信息。因此,这减轻了歧视性的性别偏见。在公共基准数据集上的实验结果表明,我们的无监督方法在不牺牲嵌入模型功能的前提下,优于当前最先进的有监督去偏词嵌入模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding
Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases.However, debias on the Chinese language, one of the most spoken languages, has been less explored.Meanwhile, existing literature relies on manually created supplementary data, which is time- and energy-consuming.In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data.Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, i.e., a kind of component in Chinese characters, during the training procedure.This consequently alleviates discriminative gender biases.Experimental results on public benchmark datasets show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.
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