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        <title>Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?</title>
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    <abstract>This work investigates the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks. For the future advance of NER in safety-critical fields like healthcare and finance, it is essential to achieve accurate predictions with calibrated confidence when applying Deep Neural Networks (DNNs), including Pre-trained Language Models (PLMs), as a real-world application. However, DNNs are prone to miscalibration, which limits their applicability. Moreover, existing methods for calibration and uncertainty estimation are computational expensive. Our investigation in NER found that data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting. Furthermore, we showed that the calibration for NER tends to be more effective when the perplexity of the sentences generated by data augmentation is lower, and that increasing the size of the augmentation further improves calibration and uncertainty.</abstract>
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%0 Conference Proceedings
%T Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?
%A Hashimoto, Wataru
%A Kamigaito, Hidetaka
%A Watanabe, Taro
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F hashimoto-etal-2024-data
%X This work investigates the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks. For the future advance of NER in safety-critical fields like healthcare and finance, it is essential to achieve accurate predictions with calibrated confidence when applying Deep Neural Networks (DNNs), including Pre-trained Language Models (PLMs), as a real-world application. However, DNNs are prone to miscalibration, which limits their applicability. Moreover, existing methods for calibration and uncertainty estimation are computational expensive. Our investigation in NER found that data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting. Furthermore, we showed that the calibration for NER tends to be more effective when the perplexity of the sentences generated by data augmentation is lower, and that increasing the size of the augmentation further improves calibration and uncertainty.
%U https://aclanthology.org/2024.emnlp-main.1049
%P 18852-18867

Markdown (Informal)

[Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?](https://aclanthology.org/2024.emnlp-main.1049) (Hashimoto et al., EMNLP 2024)

• Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation? (Hashimoto et al., EMNLP 2024)

ACL

• Wataru Hashimoto, Hidetaka Kamigaito, and Taro Watanabe. 2024. Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 18852–18867, Miami, Florida, USA. Association for Computational Linguistics.