Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder (2020.acl-main)
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| Challenge: | Existing text classification frameworks for operational risk prediction lack interpretability and labeled data are often misaligned. |
| Approach: | They propose a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for operational risk classification. |
| Outcome: | The proposed framework outperforms baseline methods on a real-world dataset and can use unlabeled data to learn visually interpretable representations. |
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