  The widespread of offensive content online such as hate speech poses a
growing societal problem. AI tools are necessary for supporting the moderation
process at online platforms. For the evaluation of these identification tools,
continuous experimentation with data sets in different languages are necessary.
The HASOC track (Hate Speech and Offensive Content Identification) is dedicated
to develop benchmark data for this purpose. This paper presents the HASOC
subtrack for English, Hindi, and Marathi. The data set was assembled from
Twitter. This subtrack has two sub-tasks. Task A is a binary classification
problem (Hate and Not Offensive) offered for all three languages. Task B is a
fine-grained classification problem for three classes (HATE) Hate speech,
OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs were
submitted by 65 teams. The performance of the best classification algorithms
for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English,
respectively. This overview presents the tasks and the data development as well
as the detailed results. The systems submitted to the competition applied a
variety of technologies. The best performing algorithms were mainly variants of
transformer architectures.
