ClaimLens: Automated, Explainable Fact-Checking on Voting Claims Using Frame-Semantics

Jacob Devasier, Rishabh Mediratta, Phuong Anh Le, David Huang, Chengkai Li


Abstract
We present ClaimLens, an automated fact-checking system focused on voting-related factual claims. Existing fact-checking solutions often lack transparency, making it difficult for users to trust and understand the reasoning behind the outcomes. In this work, we address the critical need for transparent and explainable automated fact-checking solutions. We propose a novel approach that leverages frame-semantic parsing to provide structured and interpretable fact verification. By focusing on voting-related claims, we can utilize publicly available voting records from official United States congressional sources and the established Vote semantic frame to extract relevant information from claims. Furthermore, we propose novel data augmentation techniques for frame-semantic parsing, a task known to lack robust annotated data, which leads to a +9.5% macro F1 score on frame element identification over our baseline.
Anthology ID:
2024.emnlp-demo.32
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Delia Irazu Hernandez Farias, Tom Hope, Manling Li
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
311–319
Language:
URL:
https://aclanthology.org/2024.emnlp-demo.32
DOI:
10.18653/v1/2024.emnlp-demo.32
Bibkey:
Cite (ACL):
Jacob Devasier, Rishabh Mediratta, Phuong Anh Le, David Huang, and Chengkai Li. 2024. ClaimLens: Automated, Explainable Fact-Checking on Voting Claims Using Frame-Semantics. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 311–319, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
ClaimLens: Automated, Explainable Fact-Checking on Voting Claims Using Frame-Semantics (Devasier et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-demo.32.pdf