Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic
Yakoob Khan, Weicheng Ma, Soroush Vosoughi · 2021
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 postprocessing 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 17 th place out of 91 teams in the competition.Our code is made available at https://github.com/ Yakoob-Khan/Toxic-Spans-Detection