“你可以说我是性别歧视者，但是...“：使用心理量表和对抗样本重新审视性别歧视检测
"Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples
摘要：
研究集中在有效检测在线性别歧视的自动化方法上。虽然公开的性别歧视似乎很容易发现，但其微妙的形式和多种表现却不是。在本文中，我们概述了不同层面的性别歧视接地他们在其实施的心理量表。从这些量表中，我们得出了一个社交媒体中性别歧视的密码本，我们用它来注释现有的和新的数据集，暴露出它们在性别歧视结构的广度和有效性方面的局限性。接下来，我们利用带注释的数据集生成对抗性示例，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关注一组非常狭窄的性别歧视语言标记，并且不能很好地推广到域外的例子。然而，在训练时包括不同的数据和对抗性的示例，会导致模型更好地泛化，并且对数据汇集的工件更鲁棒。通过提供一个基于规模的码本和关于最先进的缺点的见解，我们希望有助于更好和更广泛的性别歧视检测模型的发展，包括对理论驱动的数据汇集方法的反思。
英文摘要：
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.
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