  We conduct relatively extensive investigations of automatic hate speech (HS)
detection using different state-of-the-art (SoTA) baselines over 11 subtasks of
6 different datasets. Our motivation is to determine which of the recent SoTA
models is best for automatic hate speech detection and what advantage methods
like data augmentation and ensemble may have on the best model, if any. We
carry out 6 cross-task investigations. We achieve new SoTA on two subtasks -
macro F1 scores of 91.73% and 53.21% for subtasks A and B of the HASOC 2020
dataset, where previous SoTA are 51.52% and 26.52%, respectively. We achieve
near-SoTA on two others - macro F1 scores of 81.66% for subtask A of the OLID
2019 dataset and 82.54% for subtask A of the HASOC 2021 dataset, where SoTA are
82.9% and 83.05%, respectively. We perform error analysis and use two
explainable artificial intelligence (XAI) algorithms (IG and SHAP) to reveal
how two of the models (Bi-LSTM and T5) make the predictions they do by using
examples. Other contributions of this work are 1) the introduction of a simple,
novel mechanism for correcting out-of-class (OOC) predictions in T5, 2) a
detailed description of the data augmentation methods, 3) the revelation of the
poor data annotations in the HASOC 2021 dataset by using several examples and
XAI (buttressing the need for better quality control), and 4) the public
release of our model checkpoints and codes to foster transparency.
