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.

本研究评估了不同数据增强技术对低资源数据集上多标签情绪分类任务的效果，并发现后向翻译技术超过基于自编码器的方法，每个训练实例生成多个示例进一步提高了性能，此外后向翻译生成了最多样化的单字和三字组合，这些发现证明了后向翻译在资源有限情况下提升情绪分类模型性能的实用性。