Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging

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

Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging

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

University of Helsinki

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

21 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 24th Nordic Conference on Computational Linguistics
Kustantaja	University of Tartu Library
Sivut	358-365
ISBN (elektroninen)	978-9916-21-999-7
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	Nordic Conference on Computational Linguistics - Tórshavn, Färsaaret Kesto: 22 toukok. 2023 → 24 toukok. 2023 Konferenssinumero: 24

Conference

Conference	Nordic Conference on Computational Linguistics
Lyhennettä	NoDaLiDa
Maa/Alue	Färsaaret
Kaupunki	Tórshavn
Ajanjakso	22/05/2023 → 24/05/2023

Pääsy asiakirjaan

Uncertainty-Aware Natural Language Inference with Stochastic Weight AveragingFinal published version, 368 KBLisenssi: CC BY

https://aclanthology.org/2023.nodalida-1.37/Lisenssi: CC BY

Siteeraa tätä

@inproceedings{45e6f42314cd4aeea38bb3f88b5ea40a,

title = "Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging",

abstract = "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.",

author = "Aarne Talman and Hande Celikkanat and Sami Virpioja and Markus Heinonen and J{\"o}rg Tiedemann",

note = "| openaire: EC/H2020/771113/EU//FoTran ; Nordic Conference on Computational Linguistics, NoDaLiDa ; Conference date: 22-05-2023 Through 24-05-2023",

year = "2023",

language = "English",

pages = "358--365",

booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics",

publisher = "University of Tartu Library",

address = "Estonia",

}

Talman, A, Celikkanat, H, Virpioja, S, Heinonen, M & Tiedemann, J 2023, Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. julkaisussa Proceedings of the 24th Nordic Conference on Computational Linguistics. University of Tartu Library, Sivut 358-365, Nordic Conference on Computational Linguistics, Tórshavn, Färsaaret, 22/05/2023. <https://aclanthology.org/2023.nodalida-1.37/>

Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging. / Talman, Aarne; Celikkanat, Hande; Virpioja, Sami et al.
Proceedings of the 24th Nordic Conference on Computational Linguistics. University of Tartu Library, 2023. s. 358-365.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging

AU - Talman, Aarne

AU - Celikkanat, Hande

AU - Virpioja, Sami

AU - Heinonen, Markus

AU - Tiedemann, Jörg

N1 - Conference code: 24

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

M3 - Conference article in proceedings

SP - 358

EP - 365

BT - Proceedings of the 24th Nordic Conference on Computational Linguistics

PB - University of Tartu Library

T2 - Nordic Conference on Computational Linguistics

Y2 - 22 May 2023 through 24 May 2023

ER -

Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging

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Conference

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