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        <title>Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method &amp; Data Scarcity</title>
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        <namePart type="given">Jes</namePart>
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    <name type="personal">
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    <abstract>We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training models on sub-sampled datasets in three different languages, we assess the quality of estimates from a wide array of approaches and their dependence on the amount of available data. We find that while approaches based on pre-trained models and ensembles achieve the best results overall, the quality of uncertainty estimates can surprisingly suffer with more data. We also perform a qualitative analysis of uncertainties on sequences, discovering that a model’s total uncertainty seems to be influenced to a large degree by its data uncertainty, not model uncertainty. All model implementations are open-sourced in a software package.</abstract>
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%0 Conference Proceedings
%T Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity
%A Ulmer, Dennis
%A Frellsen, Jes
%A Hardmeier, Christian
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ulmer-etal-2022-exploring
%X We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training models on sub-sampled datasets in three different languages, we assess the quality of estimates from a wide array of approaches and their dependence on the amount of available data. We find that while approaches based on pre-trained models and ensembles achieve the best results overall, the quality of uncertainty estimates can surprisingly suffer with more data. We also perform a qualitative analysis of uncertainties on sequences, discovering that a model’s total uncertainty seems to be influenced to a large degree by its data uncertainty, not model uncertainty. All model implementations are open-sourced in a software package.
%R 10.18653/v1/2022.findings-emnlp.198
%U https://aclanthology.org/2022.findings-emnlp.198
%U https://doi.org/10.18653/v1/2022.findings-emnlp.198
%P 2707-2735

Markdown (Informal)

[Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity](https://aclanthology.org/2022.findings-emnlp.198) (Ulmer et al., Findings 2022)

• Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity (Ulmer et al., Findings 2022)

ACL

• Dennis Ulmer, Jes Frellsen, and Christian Hardmeier. 2022. Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2707–2735, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.