No abstract

The ACL shared task of DravidianLangTech-2022 for Troll Meme classification is a binary classification task that involves identifying Tamil memes as troll or not-troll. Classification of memes is a challenging task since memes express humour and sarcasm in an implicit way. Team SSN_MLRG1 tested and compared results obtained by using three models namely BERT, ALBERT and XLNet. The XL-Net model outperformed the other two models in terms of various performance metrics. The proposed XLNet model obtained the 3rd rank in the shared task with a weighted F1-score of 0.558.

aims to ascertain the signs of depression of a person from their messages and posts on social media wherein people share their feelings and emotions. Given social media postings in English, the system should classify the signs of depression into three labels namely "not depressed", "moderately depressed", and "severely depressed". To achieve this objective, we have adopted a fine-tuned BERT model. This solution from team SSN_MLRG1 achieves 58.5%