"Call me sexist, but..." : Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples
"Call me sexist, but..." : Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples
复制标题
“叫我性别歧视者，但是……”：使用心理量表和对抗性样本重新审视性别歧视检测
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Claudia Wagner
中科院分区：
文献类型：
--
作者：
Mattia Samory;Indira Sen;Julian Kohne;Fabian Flöck;Claudia Wagner
研究的重点是有效检测在线性别歧视的自动化方法。尽管公开的性别歧视似乎很容易被发现，但其微妙的形式和多种表达方式却并非如此。在这篇文章中，我们概述了性别歧视的不同维度，并将其应用于心理学量表中。从这些量表中，我们得出了社交媒体中性别歧视的代码本，我们使用它来注释现有的和新的数据集，暴露出它们在广度和有效性方面关于性别歧视的构造的局限性。接下来，我们利用标注后的数据集来生成对抗性示例，并测试性别歧视检测方法的可靠性。结果表明，目前的机器学习模型只接受一组非常狭窄的性别歧视的语言标记，并不能很好地概括到领域外的例子。然而，在训练时包括不同的数据和对抗性的例子会导致模型更好地泛化，并且对数据收集的人工制品更健壮。通过提供基于量表的码本和对最先进缺陷的见解，我们希望为性别歧视检测的更好和更广泛的模型的发展做出贡献，包括对理论驱动的数据收集方法的反思。
Research has focused on automated methods to effectively detect sexism online. Although overt sexism seems easy to spot, its subtle forms and manifold expressions are not. In this paper, we outline the different dimensions of sexism by grounding them in their implementation in psychological scales. From the scales, we derive a codebook for sexism in social media, which we use to annotate existing and novel datasets, surfacing their limitations in breadth and validity with respect to the construct of sexism. Next, we leverage the annotated datasets to generate adversarial examples, and test the reliability of sexism detection methods. Results indicate that current machine learning models pick up on a very narrow set of linguistic markers of sexism and do not generalize well to out-of-domain examples. Yet, including diverse data and adversarial examples at training time results in models that generalize better and that are more robust to artifacts of data collection. By providing a scale-based codebook and insights regarding the shortcomings of the state-of-the-art, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.