Abstract
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.
Users
Please
log in to take part in the discussion (add own reviews or comments).