Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder

Fan Zhou, Shengming Zhang, Yi Yang


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.78.pdf
Video:
 http://slideslive.com/38929255