Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels using Statistical Sampling and Post-Processing

• URL: http://arxiv.org/abs/2005.00295v1
• Date: Fri, 1 May 2020 10:16:40 GMT
• Title: Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels using Statistical Sampling and Post-Processing
• Authors: Manikandan Ravikiran, Amin Ekant Muljibhai, Toshinori Miyoshi, Hiroaki Ozaki, Yuta Koreeda and Sakata Masayuki
• Abstract summary: We present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. We developed a hybrid system with the BERT classifier trained with tweets selected using Statistical Sampling Algorithm (SA) and Post-Processed (PP) using an offensive wordlist. Our developed system achieved 34 th position with Macro-averaged F1-score (Macro-F1) of 0.90913 over both offensive and non-offensive classes.
• Score: 13.638230797979917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end, we developed a hybrid system with the BERT classifier trained with tweets selected using Statistical Sampling Algorithm (SA) and Post-Processed (PP) using an offensive wordlist. Our developed system achieved 34 th position with Macro-averaged F1-score (Macro-F1) of 0.90913 over both offensive and non-offensive classes. We further show comprehensive results and error analysis to assist future research in offensive language identification with noisy labels.

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