Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data
Table 3: Class-wise precision and recall with and without oversampling (OS) achieved on unseen part of the training set.
['[EMPTY]', '[BOLD] OS', '[BOLD] No OS']
So as to better understand the aspects of oversampling that contribute to these gains, we perform a class-wise performance analysis of BERT with/without oversampling. This is achieved by significantly improving the recall of the minority class (propaganda) at the cost of the recall of the majority class.