  Hope Speech Detection, a task of recognizing positive expressions, has made
significant strides recently. However, much of the current works focus on model
development without considering the issue of inherent imbalance in the data.
Our work revisits this issue in hope-speech detection by introducing focal
loss, data augmentation, and pre-processing strategies. Accordingly, we find
that introducing focal loss as part of Multilingual-BERT's (M-BERT) training
process mitigates the effect of class imbalance and improves overall F1-Macro
by 0.11. At the same time, contextual and back-translation-based word
augmentation with M-BERT improves results by 0.10 over baseline despite
imbalance. Finally, we show that overlapping word removal based on
pre-processing, though simple, improves F1-Macro by 0.28. In due process, we
present detailed studies depicting various behaviors of each of these
strategies and summarize key findings from our empirical results for those
interested in getting the most out of M-BERT for hope speech detection under
real-world conditions of data imbalance.
