Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity

Dennis Ulmer, Jes Frellsen, Christian Hardmeier


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.
Anthology ID:
2022.findings-emnlp.198
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2707–2735
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.198
DOI:
10.18653/v1/2022.findings-emnlp.198
Bibkey:
Cite (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.
Cite (Informal):
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity (Ulmer et al., Findings 2022)
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