QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking

Liangming Pan, Xinyuan Lu, Min-Yen Kan, Preslav Nakov


Abstract
Fact-checking real-world claims often requires intricate, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking systems often lack transparency in their decision-making, making it challenging for users to comprehend their reasoning process. To address this, we propose the Question-guided Multi-hop Fact-Checking (QACheck) system, which guides the model’s reasoning process by asking a series of questions critical for verifying a claim. QACheck has five key modules: a claim verifier, a question generator, a question-answering module, a QA validator, and a reasoner. Users can input a claim into QACheck, which then predicts its veracity and provides a comprehensive report detailing its reasoning process, guided by a sequence of (question, answer) pairs. QACheck also provides the source of evidence supporting each question, fostering a transparent, explainable, and user-friendly fact-checking process.
Anthology ID:
2023.emnlp-demo.23
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
December
Year:
2023
Address:
Singapore
Editors:
Yansong Feng, Els Lefever
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
264–273
Language:
URL:
https://aclanthology.org/2023.emnlp-demo.23
DOI:
10.18653/v1/2023.emnlp-demo.23
Bibkey:
Cite (ACL):
Liangming Pan, Xinyuan Lu, Min-Yen Kan, and Preslav Nakov. 2023. QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 264–273, Singapore. Association for Computational Linguistics.
Cite (Informal):
QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking (Pan et al., EMNLP 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/improve-issue-templates/2023.emnlp-demo.23.pdf
Video:
 https://preview.aclanthology.org/improve-issue-templates/2023.emnlp-demo.23.mp4