Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging

Aarne Talman, Hande Celikkanat, Sami Virpioja, Markus Heinonen, Jörg Tiedemann

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

31 Downloads (Pure)


This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. We apply the approach to standard tasks in natural language inference (NLI) and demonstrate the effectiveness of the method in terms of prediction accuracy and correlation with human annotation disagreements. We argue that the uncertainty representations in SWAG better reflect subjective interpretation and the natural variation that is also present in human language understanding. The results reveal the importance of uncertainty modeling, an often neglected aspect of neural language modeling, in NLU tasks.
Original languageEnglish
Title of host publicationProceedings of the 24th Nordic Conference on Computational Linguistics
PublisherUniversity of Tartu Library
ISBN (Electronic)978-9916-21-999-7
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventNordic Conference on Computational Linguistics - Tórshavn, Faroe Islands
Duration: 22 May 202324 May 2023
Conference number: 24


ConferenceNordic Conference on Computational Linguistics
Abbreviated titleNoDaLiDa
Country/TerritoryFaroe Islands


Dive into the research topics of 'Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging'. Together they form a unique fingerprint.

Cite this