Abstract
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.- Anthology ID:
- 2020.acl-main.78
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 846–852
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.78
- DOI:
- 10.18653/v1/2020.acl-main.78
- Cite (ACL):
- Fan Zhou, Shengming Zhang, and Yi Yang. 2020. Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 846–852, Online. Association for Computational Linguistics.
- Cite (Informal):
- Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder (Zhou et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.acl-main.78.pdf