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Efficient Uncertainty Quantification for Multilabel Text Classification

Yu, Jialin; Cristea, Alexandra I.; Harit, Anoushka; Sun, Zhongtian; Aduragba, Olanrewaju Tahir; Shi, Lei; Al Moubayed, Noura

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Jialin Yu
Academic Visitor

Zhongtian Sun

Lei Shi


Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty quantification methods. Existing approaches to uncertainty quantification in natural language processing (NLP) take a Bayesian Deep Learning approach. However, the latter is known to not be computationally efficient in testing time, thus hindering its applicability in real-life scenarios. This paper proposes a new focus on the efficiency of uncertainty quantification methods, evaluating them on four multi-label text classification tasks. Our novel methods of representing epistemic and aleatoric uncertainties enable efficient uncertainty quantification (around 13 to 45 times faster than existing approaches, depending on architecture) with posterior analysis in the (approximated) latent- and data space. We conduct extensive experiments and studies on diverse neural network architectures (LSTM, CNN and Transformer) to analyse their power. Our results prove the benefits of explicitly modelling uncertainty in neural networks.


Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022). Efficient Uncertainty Quantification for Multilabel Text Classification. .

Conference Name 2022 International Joint Conference on Neural Networks (IJCNN)
Conference Location Padova, Italy
Start Date Jul 18, 2022
End Date Jul 23, 2022
Acceptance Date Apr 26, 2022
Online Publication Date Sep 30, 2022
Publication Date 2022
Deposit Date Aug 31, 2022
Publicly Available Date Sep 1, 2022
Series ISSN 2161-4393,2161-4407


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