Yu, Jialin, Cristea, Alexandra I., Harit, Anoushka, Sun, Zhongtian, Aduragba, Olanrewaju Tahir, Shi, Lei, Moubayed, Noura Al (2022) Efficient Uncertainty Quantification for Multilabel Text Classification. In: 2022 International Joint Conference on Neural Networks (IJCNN). . IEEE ISBN 978-1-7281-8671-9. (doi:10.1109/IJCNN55064.2022.9892871) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:108677)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication) | |
Official URL: https://doi.org/10.1109/IJCNN55064.2022.9892871 |
Abstract
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.
Item Type: | Conference or workshop item (Proceeding) |
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DOI/Identification number: | 10.1109/IJCNN55064.2022.9892871 |
Subjects: | Q Science > Q Science (General) > Q335 Artificial intelligence |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Zhongtian Sun |
Date Deposited: | 06 Feb 2025 16:35 UTC |
Last Modified: | 10 Feb 2025 22:26 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/108677 (The current URI for this page, for reference purposes) |
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