论文标题
性别偏见在单词嵌入对抑郁预测的影响
The effects of gender bias in word embeddings on depression prediction
论文作者
论文摘要
单词嵌入在各种NLP问题中广泛使用，作为最新的语义特征向量表示。尽管它们在各种任务和领域上都取得了成功，但由于统计和社会偏见的数据集中存在培训，它们可能对刻板印象类别表现出不希望的偏见。在这项研究中，我们分析了针对精神障碍域中专门针对抑郁类别的四个不同预训练的单词嵌入的性别偏见。我们使用对域独立和临床领域特异性数据进行训练的上下文和非上下文嵌入。我们观察到，嵌入抑郁症会偏向于不同的性别群体，具体取决于嵌入的类型。此外，我们证明了这些不希望的相关性被转移到抑郁型表型识别的下游任务中。我们发现，通过简单地交换性别单词来减轻下游任务中的偏见，我们发现数据增加。
Word embeddings are extensively used in various NLP problems as a state-of-the-art semantic feature vector representation. Despite their success on various tasks and domains, they might exhibit an undesired bias for stereotypical categories due to statistical and societal biases that exist in the dataset they are trained on. In this study, we analyze the gender bias in four different pre-trained word embeddings specifically for the depression category in the mental disorder domain. We use contextual and non-contextual embeddings that are trained on domain-independent as well as clinical domain-specific data. We observe that embeddings carry bias for depression towards different gender groups depending on the type of embeddings. Moreover, we demonstrate that these undesired correlations are transferred to the downstream task for depression phenotype recognition. We find that data augmentation by simply swapping gender words mitigates the bias significantly in the downstream task.
