JU at HASOC 2020: Deep Learning with RoBERTa and Random Forest for Hate Speech and Offensive Content Identification in Indo European Languages
The identification of Hate Speech in Social Media has received much attention in research recently. There has been an ever growing increase in demand particularly for research in languages other than English. The Hate Speech and Offensive Content (HASOC) track has created resources for Hate Speech Identification in three different languages namely Hindi, German, and English. We have participated in both Sub tasks A and B of the 2020 shared task on hate speech and offensive content identification in Indo European languages. Our approach relies on a combined model of multilingual RoBERTa (a Robustly Optimized BERT Pretraining Approach) model with pre trained vectors and a Random Forest model using Word2Vec, TF IDF, and other textual features as input. Our system has achieved a maximum Macro F1 score of 50.28% for English Sub task A which is quite satisfactory relative to the performance of other systems and secured 8th position among participating teams.
FIRE