  Data augmentation has the potential to improve the performance of machine
learning models by increasing the amount of training data available. In this
study, we evaluated the effectiveness of different data augmentation techniques
for a multi-label emotion classification task using a low-resource dataset. Our
results showed that Back Translation outperformed autoencoder-based approaches
and that generating multiple examples per training instance led to further
performance improvement. In addition, we found that Back Translation generated
the most diverse set of unigrams and trigrams. These findings demonstrate the
utility of Back Translation in enhancing the performance of emotion
classification models in resource-limited situations.
