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        <title>Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder</title>
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            <namePart type="given">Dan</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
%A Zhou, Fan
%A Zhang, Shengming
%A Yang, Yi
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2020-interpretable
%X 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.
%R 10.18653/v1/2020.acl-main.78
%U https://aclanthology.org/2020.acl-main.78
%U https://doi.org/10.18653/v1/2020.acl-main.78
%P 846-852

Markdown (Informal)

[Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder](https://aclanthology.org/2020.acl-main.78) (Zhou et al., ACL 2020)

• Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder (Zhou et al., ACL 2020)

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.