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This paper describes our approach to the Toxic Spans Detection problem(SemEval-2021 Task 5). We propose BERToxic, a system that fine-tunes apre-trained BERT model to locate toxic text spans in a given text and utilizesadditional post-processing steps to refine the boundaries. The post-processingsteps involve (1) labeling character offsets between consecutive toxic tokensas toxic and (2) assigning a toxic label to words that have at least one tokenlabeled as toxic. Through experiments, we show that these two post-processingsteps improve the performance of our model by 4.16% on the test set. We alsostudied the effects of data augmentation and ensemble modeling strategies onour system. Our system significantly outperformed the provided baseline andachieved an F1-score of 0.683, placing Lone Pine in the 17th place out of 91teams in the competition. Our code is made available athttps://github.com/Yakoob-Khan/Toxic-Spans-Detection

Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic