Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic
Abstract
This paper describes our approach to the Toxic Spans Detection problem (SemEval-2021 Task 5). We propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic text spans in a given text and utilizes additional post-processing steps to refine the boundaries. The post-processing steps involve (1) labeling character offsets between consecutive toxic tokens as toxic and (2) assigning a toxic label to words that have at least one token labeled as toxic. Through experiments, we show that these two post-processing steps improve the performance of our model by 4.16% on the test set. We also studied the effects of data augmentation and ensemble modeling strategies on our system. Our system significantly outperformed the provided baseline and achieved an F1-score of 0.683, placing Lone Pine in the 17th place out of 91 teams in the competition. Our code is made available at https://github.com/Yakoob-Khan/Toxic-Spans-Detection
Keywords
Cite
@article{arxiv.2104.03506,
title = {Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic},
author = {Yakoob Khan and Weicheng Ma and Soroush Vosoughi},
journal= {arXiv preprint arXiv:2104.03506},
year = {2021}
}
Comments
7 pages, 3 figures. Accepted at SemEval-2021 Workshop, ACL-IJCNLP 2021