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    <abstract>Hate speech detection on social media platforms is crucial as it helps to avoid severe situations, and severe harm to marginalized people and groups. The application of Natural Language Processing(NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection. The expression of hate, however is varied and ever evolving. Thus, it is important for better detection systems to adapt to this variance. Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions. In this paper, we discuss our entry to one such competition, namely the English version of sub-task A for the OffensEval competition. Our contribution can be perceived through our results, which were first a F1-score of 0.9089, and with further refinements described here climb up to0.9166. It serves to give more support to our hypothesis that one of the variants of BERT (Devlin et al., 2018), namely RoBERTa can successfully differentiate between offensive and not-offensive tweets, given some preprocessing steps (also outlined here).</abstract>
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%0 Conference Proceedings
%T TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa
%A Alonso, Pedro
%A Saini, Rajkumar
%A Kovacs, György
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F alonso-etal-2020-thenorth
%X Hate speech detection on social media platforms is crucial as it helps to avoid severe situations, and severe harm to marginalized people and groups. The application of Natural Language Processing(NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection. The expression of hate, however is varied and ever evolving. Thus, it is important for better detection systems to adapt to this variance. Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions. In this paper, we discuss our entry to one such competition, namely the English version of sub-task A for the OffensEval competition. Our contribution can be perceived through our results, which were first a F1-score of 0.9089, and with further refinements described here climb up to0.9166. It serves to give more support to our hypothesis that one of the variants of BERT (Devlin et al., 2018), namely RoBERTa can successfully differentiate between offensive and not-offensive tweets, given some preprocessing steps (also outlined here).
%R 10.18653/v1/2020.semeval-1.292
%U https://aclanthology.org/2020.semeval-1.292/
%U https://doi.org/10.18653/v1/2020.semeval-1.292
%P 2197-2202

Markdown (Informal)

[TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa](https://aclanthology.org/2020.semeval-1.292/) (Alonso et al., SemEval 2020)

• TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa (Alonso et al., SemEval 2020)

ACL

• Pedro Alonso, Rajkumar Saini, and György Kovacs. 2020. TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2197–2202, Barcelona (online). International Committee for Computational Linguistics.