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Supervised approaches generally rely on majority-based labels. However, it ishard to achieve high agreement among annotators in subjective tasks such ashate speech detection. Existing neural network models principally regard labelsas categorical variables, while ignoring the semantic information in diverselabel texts. In this paper, we propose AnnoBERT, a first-of-its-kindarchitecture integrating annotator characteristics and label text with atransformer-based model to detect hate speech, with unique representationsbased on each annotator's characteristics via Collaborative Topic Regression(CTR) and integrate label text to enrich textual representations. Duringtraining, the model associates annotators with their label choices given apiece of text; during evaluation, when label information is not available, themodel predicts the aggregated label given by the participating annotators byutilising the learnt association. The proposed approach displayed an advantagein detecting hate speech, especially in the minority class and edge cases withannotator disagreement. Improvement in the overall performance is the largestwhen the dataset is more label-imbalanced, suggesting its practical value inidentifying real-world hate speech, as the volume of hate speech in-the-wild isextremely small on social media, when compared with normal (non-hate) speech.Through ablation studies, we show the relative contributions of annotatorembeddings and label text to the model performance, and tested a range ofalternative annotator embeddings and label text combinations.

AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection