Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning

Aalok Sathe, Joonsuk Park


Abstract
Automatic fact-checking is crucial for recognizing misinformation spreading on the internet. Most existing fact-checkers break down the process into several subtasks, one of which determines candidate evidence sentences that can potentially support or refute the claim to be verified; typically, evidence sentences with gold-standard labels are needed for this. In a more realistic setting, however, such sentence-level annotations are not available. In this paper, we tackle the natural language inference (NLI) subtask—given a document and a (sentence) claim, determine whether the document supports or refutes the claim—only using document-level annotations. Using fine-tuned BERT and multiple instance learning, we achieve 81.9% accuracy, significantly outperforming the existing results on the WikiFactCheck-English dataset.
Anthology ID:
2021.fever-1.11
Volume:
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2021
Address:
Dominican Republic
Venues:
EMNLP | FEVER
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–107
Language:
URL:
https://aclanthology.org/2021.fever-1.11
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.fever-1.11.pdf
Data
FEVER