  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.
