@inproceedings{van-niekerk-etal-2021-uncertainty,
title = "Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance",
author = "van Niekerk, Carel and
Malinin, Andrey and
Geishauser, Christian and
Heck, Michael and
Lin, Hsien-chin and
Lubis, Nurul and
Feng, Shutong and
Gasic, Milica",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.623",
doi = "10.18653/v1/2021.emnlp-main.623",
pages = "7901--7914",
abstract = "The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking. However, such systems consider only confidence estimates and have difficulty scaling to more complex settings. Neural dialogue systems, on the other hand, rarely take uncertainties into account. They are therefore overconfident in their decisions and less robust. Moreover, the performance of the tracking task is often evaluated in isolation, without consideration of its effect on the downstream policy optimisation. We propose the use of different uncertainty measures in neural belief tracking. The effects of these measures on the downstream task of policy optimisation are evaluated by adding selected measures of uncertainty to the feature space of the policy and training policies through interaction with a user simulator. Both human and simulated user results show that incorporating these measures leads to improvements both of the performance and of the robustness of the downstream dialogue policy. This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.",
}
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<abstract>The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking. However, such systems consider only confidence estimates and have difficulty scaling to more complex settings. Neural dialogue systems, on the other hand, rarely take uncertainties into account. They are therefore overconfident in their decisions and less robust. Moreover, the performance of the tracking task is often evaluated in isolation, without consideration of its effect on the downstream policy optimisation. We propose the use of different uncertainty measures in neural belief tracking. The effects of these measures on the downstream task of policy optimisation are evaluated by adding selected measures of uncertainty to the feature space of the policy and training policies through interaction with a user simulator. Both human and simulated user results show that incorporating these measures leads to improvements both of the performance and of the robustness of the downstream dialogue policy. This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.</abstract>
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%0 Conference Proceedings
%T Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance
%A van Niekerk, Carel
%A Malinin, Andrey
%A Geishauser, Christian
%A Heck, Michael
%A Lin, Hsien-chin
%A Lubis, Nurul
%A Feng, Shutong
%A Gasic, Milica
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F van-niekerk-etal-2021-uncertainty
%X The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking. However, such systems consider only confidence estimates and have difficulty scaling to more complex settings. Neural dialogue systems, on the other hand, rarely take uncertainties into account. They are therefore overconfident in their decisions and less robust. Moreover, the performance of the tracking task is often evaluated in isolation, without consideration of its effect on the downstream policy optimisation. We propose the use of different uncertainty measures in neural belief tracking. The effects of these measures on the downstream task of policy optimisation are evaluated by adding selected measures of uncertainty to the feature space of the policy and training policies through interaction with a user simulator. Both human and simulated user results show that incorporating these measures leads to improvements both of the performance and of the robustness of the downstream dialogue policy. This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.
%R 10.18653/v1/2021.emnlp-main.623
%U https://aclanthology.org/2021.emnlp-main.623
%U https://doi.org/10.18653/v1/2021.emnlp-main.623
%P 7901-7914
Markdown (Informal)
[Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance](https://aclanthology.org/2021.emnlp-main.623) (van Niekerk et al., EMNLP 2021)
ACL
- Carel van Niekerk, Andrey Malinin, Christian Geishauser, Michael Heck, Hsien-chin Lin, Nurul Lubis, Shutong Feng, and Milica Gasic. 2021. Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7901–7914, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.