Combating high variance in Data-Scarce Implicit Hate Speech Classification

• URL: http://arxiv.org/abs/2208.13595v1
• Date: Mon, 29 Aug 2022 13:45:21 GMT
• Title: Combating high variance in Data-Scarce Implicit Hate Speech Classification
• Authors: Debaditya Pal, Kaustubh Chaudhari, Harsh Sharma
• Abstract summary: We develop a novel RoBERTa-based model that achieves state-of-the-art performance. In this paper, we explore various optimization and regularization techniques and develop a novel RoBERTa-based model that achieves state-of-the-art performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hate speech classification has been a long-standing problem in natural language processing. However, even though there are numerous hate speech detection methods, they usually overlook a lot of hateful statements due to them being implicit in nature. Developing datasets to aid in the task of implicit hate speech classification comes with its own challenges; difficulties are nuances in language, varying definitions of what constitutes hate speech, and the labor-intensive process of annotating such data. This had led to a scarcity of data available to train and test such systems, which gives rise to high variance problems when parameter-heavy transformer-based models are used to address the problem. In this paper, we explore various optimization and regularization techniques and develop a novel RoBERTa-based model that achieves state-of-the-art performance.

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