HateBERT: Retraining BERT for Abusive Language Detection in English
This work addresses the problem of detecting abusive language online for researchers and practitioners, but it is incremental as it adapts an existing model to a specific domain.
The authors tackled abusive language detection in English by retraining BERT on a dataset from banned Reddit communities, resulting in HateBERT outperforming general BERT across three datasets for offensive, abusive, and hate speech detection tasks.
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have collected and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the abuse-inclined version obtained by retraining with posts from the banned communities on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the generic pre-trained language model and its corresponding abusive language-inclined counterpart across the datasets, indicating that portability is affected by compatibility of the annotated phenomena.