ArMIS - The Arabic Misogyny and Sexism Corpus with Annotator Subjective Disagreements
ArMIS - The Arabic Misogyny and Sexism Corpus with Annotator Subjective Disagreements
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ArMIS - 带有注释者主观分歧的阿拉伯厌女症和性别歧视语料库
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Massimo Poesio
中科院分区：
文献类型：
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作者：
Dina Almanea;Massimo Poesio
近年来，社交媒体上厌恶女性和性别歧视语言的使用有所增加，在阿拉伯世界，为了应对试图取消对妇女生活限制的改革，这种语言的使用也在增加。然而，有几个基准阿拉伯厌女症和性别歧视检测，并在这些注释是在汇总的形式，即使厌女症和性别歧视的判断被发现是高度主观的。在本文中，我们介绍了一个阿拉伯语厌女症和性别歧视数据集（ArMIS）的特点是提供不同程度的宗教信仰的注释者的注释，并提供证据表明，这种差异确实会导致分歧。据我们所知，这是第一个详细研究信仰对厌女症和性别歧视注释影响的数据集。我们还讨论了概念验证实验，这些实验表明，一个没有调和分歧的数据集可以用来训练最先进的厌女症和性别歧视检测模型;并考虑评估这些模型的不同方式。
The use of misogynistic and sexist language has increased in recent years in social media, and is increasing in the Arabic world in reaction to reforms attempting to remove restrictions on women lives. However, there are few benchmarks for Arabic misogyny and sexism detection, and in those the annotations are in aggregated form even though misogyny and sexism judgments are found to be highly subjective. In this paper we introduce an Arabic misogyny and sexism dataset (ArMIS) characterized by providing annotations from annotators with different degree of religious beliefs, and provide evidence that such differences do result in disagreements. To the best of our knowledge, this is the first dataset to study in detail the effect of beliefs on misogyny and sexism annotation. We also discuss proof-of-concept experiments showing that a dataset in which disagreements have not been reconciled can be used to train state-of-the-art models for misogyny and sexism detection; and consider different ways in which such models could be evaluated.
影响因子：
1.4
作者：
Peng Bi
通讯作者：
Peng Bi