Estimating predictive uncertainty for rumour verification models

Elena Kochkina, Maria Liakata


Abstract
The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. We show that these estimates can be used to filter out model predictions likely to be erroneous so that these difficult instances can be prioritised by a human fact-checker. We propose two methods for uncertainty-based instance rejection, supervised and unsupervised. We also show how uncertainty estimates can be used to interpret model performance as a rumour unfolds.
Anthology ID:
2020.acl-main.623
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6964–6981
Language:
URL:
https://aclanthology.org/2020.acl-main.623
DOI:
10.18653/v1/2020.acl-main.623
Bibkey:
Cite (ACL):
Elena Kochkina and Maria Liakata. 2020. Estimating predictive uncertainty for rumour verification models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6964–6981, Online. Association for Computational Linguistics.
Cite (Informal):
Estimating predictive uncertainty for rumour verification models (Kochkina & Liakata, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.623.pdf
Video:
 http://slideslive.com/38929128
Code
 kochkinaelena/Uncertainty4VerificationModels