SOTAVerified

Estimating predictive uncertainty for rumour verification models

2020-05-14ACL 2020Code Available1· sign in to hype

Elena Kochkina, Maria Liakata

Code Available — Be the first to reproduce this paper.

Reproduce

Code

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.

Tasks

Reproductions