In this paper, we describe our approach towards utilizing pre-trained models for the task of hope speech detection. We participated in Task 2: Hope Speech Detection for Equality, Diversity and Inclusion at LT-EDI-2021 @ EACL2021. The goal of this tas
k is to predict the presence of hope speech, along with the presence of samples that do not belong to the same language in the dataset. We describe our approach to fine-tuning RoBERTa for Hope Speech detection in English and our approach to fine-tuning XLM-RoBERTa for Hope Speech detection in Tamil and Malayalam, two low resource Indic languages. We demonstrate the performance of our approach on classifying text into hope-speech, non-hope and not-language. Our approach ranked 1st in English (F1 = 0.93), 1st in Tamil (F1 = 0.61) and 3rd in Malayalam (F1 = 0.83).
