  Hate speech detection has become an important research topic within the past
decade. More private corporations are needing to regulate user generated
content on different platforms across the globe. In this paper, we introduce a
study of multilingual hate speech classification. We compile a dataset of 11
languages and resolve different taxonomies by analyzing the combined data with
binary labels: hate speech or not hate speech. Defining hate speech in a single
way across different languages and datasets may erase cultural nuances to the
definition, therefore, we utilize language agnostic embeddings provided by
LASER and MUSE in order to develop models that can use a generalized definition
of hate speech across datasets. Furthermore, we evaluate prior state of the art
methodologies for hate speech detection under our expanded dataset. We conduct
three types of experiments for a binary hate speech classification task:
Multilingual-Train Monolingual-Test, MonolingualTrain Monolingual-Test and
Language-Family-Train Monolingual Test scenarios to see if performance
increases for each language due to learning more from other language data.
