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This paper aims to describe the approach we used to detect hope speech in theHopeEDI dataset. We experimented with two approaches. In the first approach, weused contextual embeddings to train classifiers using logistic regression,random forest, SVM, and LSTM based models.The second approach involved using amajority voting ensemble of 11 models which were obtained by fine-tuningpre-trained transformer models (BERT, ALBERT, RoBERTa, IndicBERT) after addingan output layer. We found that the second approach was superior for English,Tamil and Malayalam. Our solution got a weighted F1 score of 0.93, 0.75 and0.49 for English,Malayalam and Tamil respectively. Our solution ranked first inEnglish, eighth in Malayalam and eleventh in Tamil.

Hopeful_Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers