In recent times, internet users are frequently exposed to Hate Speech on social media platforms that have long-lasting negative impacts on their mental wellbeing and also radicalizes the society into an environment of fear and distrust. Many methods have been developed to detect and stop propagation of Hate Speech. However, there is a limitation of annotated data available for Hate Speech in Bengali language. In this work, we have used a pretrained BanglaBERT model on an extended train dataset synthesized via data augmentation techniques. Our team Bahash-AI has achieved 20th, 20th and 17th position of the 3 subtasks out of total 37, 24 and 21 total number of teams who participated in the subtasks 1A, 1B and 1C respectively for Bangla Multi-task Hatespeech Identification Shared Task at BLP Workshop with F1 scores of 0.7028, 0.6954, 0.6969 respectively.

The neural machine translation approach has gained popularity in machine translation because of its context analysing ability and its handling of long-term dependency issues. We have participated in the WMT21 shared task of similar language translation on a Tamil-Telugu pair with the team name: CNLP-NITS. In this task, we utilized monolingual data via pre-train word embeddings in transformer model based neural machine translation to tackle the limitation of parallel corpus. Our model has achieved a bilingual evaluation understudy (BLEU) score of 4.05, rank-based intuitive bilingual evaluation score (RIBES) score of 24.80 and translation edit rate (TER) score of 97.24 for both Tamil-to-Telugu and Telugu-to-Tamil translations respectively.