SS-BERT：利用“主观性”和“身份词”的概念减轻有毒评论分类中的身份词偏见
SS-BERT: Mitigating Identity Terms Bias in Toxic Comment Classification by Utilising the Notion of "Subjectivity" and "Identity Terms"
摘要：
有毒评论分类模型经常被发现偏向于身份术语，这些术语是表征特定人群的术语，例如“穆斯林”和“黑人”。这种偏见通常反映在假阳性预测中，即带有身份术语的无毒评论。在这项工作中，我们提出了一种新的方法来解决有毒评论分类中的这种偏见，利用评论的主观性水平和身份术语的存在的概念。我们假设，当一个评论是关于一群人的特征是由一个身份术语，该评论是有毒的可能性与评论的主观性水平，即在何种程度上的评论传达个人的感情和意见。在BERT模型的基础上，我们提出了一个新的结构，能够利用这些功能，并在4个不同大小的数据集上彻底评估我们的模型，并代表不同的社交媒体平台。结果表明，我们的模型可以始终优于BERT和SOTA模型设计，以解决同一性术语偏见以不同的方式，在F1的最大改善分别为2.43%和1.91%。
英文摘要：
Toxic comment classification models are often found biased toward identity terms which are terms characterizing a specific group of people such as "Muslim" and "black". Such bias is commonly reflected in false-positive predictions, i.e. non-toxic comments with identity terms. In this work, we propose a novel approach to tackle such bias in toxic comment classification, leveraging the notion of subjectivity level of a comment and the presence of identity terms. We hypothesize that when a comment is made about a group of people that is characterized by an identity term, the likelihood of that comment being toxic is associated with the subjectivity level of the comment, i.e. the extent to which the comment conveys personal feelings and opinions. Building upon the BERT model, we propose a new structure that is able to leverage these features, and thoroughly evaluate our model on 4 datasets of varying sizes and representing different social media platforms. The results show that our model can consistently outperform BERT and a SOTA model devised to address identity term bias in a different way, with a maximum improvement in F1 of 2.43% and 1.91% respectively.
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