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.

本研究利用聚焦损失、数据增强和预处理策略等方式，解决了积极表达检测中数据不平衡的问题，并通过详细研究发现，将聚焦损失应用于多语言 BERT 的训练过程中，弥补了类别不平衡的影响，提高了整体的 F1-Macro 值，而基于上下文和反向翻译的词语增强和基于预处理的去重复方法也分别使结果比基线模型提升了 0.10 和 0.28。本研究总结了每种方法的性能及实现过程，适用于在真实世界下解决希望言论检测中数据不平衡的问题。

剔除重叠词：再探希望言论检测中的数据不平衡