Disentangled and Robust Representation Learning for Bragging Classification in Social Media
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, 00, pp. 1-5
- Issue Date:
- 2023-05-05
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Disentangled and Robust Representation Learning for Bragging Classification in Social Media.pdf | Published version | 1.28 MB |
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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
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