  Researching bragging behavior on social media arouses interest of
computational (socio) linguists. However, existing bragging classification
datasets suffer from a serious data imbalance issue. Because labeling a
data-balance dataset is expensive, most methods introduce external knowledge to
improve model learning. Nevertheless, such methods inevitably introduce noise
and non-relevance information from external knowledge. To overcome the
drawback, we propose a novel bragging classification method with
disentangle-based representation augmentation and domain-aware adversarial
strategy. Specifically, model learns to disentangle and reconstruct
representation and generate augmented features via disentangle-based
representation augmentation. Moreover, domain-aware adversarial strategy aims
to constrain domain of augmented features to improve their robustness.
Experimental results demonstrate that our method achieves state-of-the-art
performance compared to other methods.
