Shengming Zhang


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Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
Fan Zhou | Shengming Zhang | Yi Yang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction, including imbalanced labeled/unlabeled data and lacking interpretability. To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC). We empirically evaluate the framework on a real-world dataset. The results demonstrate that our method can better utilize unlabeled data and learn visually interpretable document representations. SemiORC also outperforms other baseline methods on operational risk classification.