A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification

• URL: http://arxiv.org/abs/2202.02511v1
• Date: Sat, 5 Feb 2022 08:09:09 GMT
• Title: A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification
• Authors: Yves Bestgen
• Abstract summary: A system based on a classical supervised algorithm only fed with character n-grams, and thus completely language-agnostic, is proposed. It reached a medium performance level in English, the language for which it is easy to develop deep learning approaches. It ends even first when performances are averaged over the three tasks in these languages, outperforming many deep learning approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For automatically identifying hate speech and offensive content in tweets, a system based on a classical supervised algorithm only fed with character n-grams, and thus completely language-agnostic, is proposed by the SATLab team. After its optimization in terms of the feature weighting and the classifier parameters, it reached, in the multilingual HASOC 2021 challenge, a medium performance level in English, the language for which it is easy to develop deep learning approaches relying on many external linguistic resources, but a far better level for the two less resourced language, Hindi and Marathi. It ends even first when performances are averaged over the three tasks in these languages, outperforming many deep learning approaches. These performances suggest that it is an interesting reference level to evaluate the benefits of using more complex approaches such as deep learning or taking into account complementary resources.

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