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

• URL: http://arxiv.org/abs/2104.03506v1
• Date: Thu, 8 Apr 2021 04:46:14 GMT
• Title: Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic
• Authors: Yakoob Khan, Weicheng Ma, Soroush Vosoughi
• Abstract summary: This paper describes our approach to the Toxic Spans Detection problem. We propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic text spans in a given text. 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.
• Score: 2.4815579733050153
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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