In this paper, we tackle the Arabic Fine-Grained Hate Speech Detection shared task and demonstrate significant improvements over reported baselines for its three subtasks. The tasks are to predict if a tweet contains (1) Offensive language; and whether it is considered (2) Hate Speech or not and if so, then predict the (3) Fine-Grained Hate Speech label from one of six categories. Our final solution is an ensemble of models that employs multitask learning and a self-consistency correction method yielding 82.7% on the hate speech subtask -- reflecting a 3.4% relative improvement compared to previous work.
@article{arxiv.2205.07960,
title = {Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification},
author = {Badr AlKhamissi and Mona Diab},
journal= {arXiv preprint arXiv:2205.07960},
year = {2022}
}
Comments
Accepted at the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5/LREC 2022)