From Chaos to Clarity: Claim Normalization to Empower Fact-Checking

Megha Sundriyal, Tanmoy Chakraborty, Preslav Nakov

Abstract
With the proliferation of social media platforms, users are exposed to vast information, including posts containing misleading claims. However, the pervasive noise inherent in these posts presents a challenge in identifying precise and prominent claims that require verification. Extracting the core assertions from such posts is arduous and time-consuming. We introduce a novel task, called Claim Normalization (aka ClaimNorm) that aims to decompose complex and noisy social media posts into more straightforward and understandable forms, termed normalized claims. We propose CACN , a pioneering approach that leverages chain-of-thought and claim check-worthiness estimation, mimicking human reasoning processes, to comprehend intricate claims. Moreover, we capitalize on large language models’ powerful in-context learning abilities to provide guidance and improve the claim normalization process. To evaluate the effectiveness of our proposed model, we meticulously compile a comprehensive real-world dataset, CLAN, comprising more than 6k instances of social media posts alongside their respective normalized claims. Experimentation demonstrates that CACN outperforms several baselines across various evaluation measures. A rigorous error analysis validates CACN‘s capabilities and pitfalls. We release our dataset and code at https://github.com/LCS2-IIITD/CACN-EMNLP-2023.
Anthology ID:
2023.findings-emnlp.439
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6594–6609
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/2023.findings-emnlp.439/
DOI:
10.18653/v1/2023.findings-emnlp.439
Bibkey:
Cite (ACL):
Megha Sundriyal, Tanmoy Chakraborty, and Preslav Nakov. 2023. From Chaos to Clarity: Claim Normalization to Empower Fact-Checking. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6594–6609, Singapore. Association for Computational Linguistics.
Cite (Informal):
From Chaos to Clarity: Claim Normalization to Empower Fact-Checking (Sundriyal et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/icon-24-ingestion/2023.findings-emnlp.439.pdf
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