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).
This paper describes approaches to identify Hope Speech in short, informal texts in English, Malayalam and Tamil using different machine learning techniques. We demonstrate that even very simple baseline algorithms perform reasonably well on this tas
k if provided with enough training data. However, our best performing algorithm is a cross-lingual transfer learning approach in which we fine-tune XLM-RoBERTa.
