Disentangled and Robust Representation Learning for Bragging Classification in Social Media
This addresses data imbalance for computational sociolinguists, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of data imbalance in bragging classification on social media by proposing a method with disentangle-based representation augmentation and domain-aware adversarial strategy, achieving state-of-the-art performance.
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.