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

摘要

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 (\textit{aka ClaimNorm}) that aims to decompose complex and noisy social media posts into more straightforward and understandable forms, termed \textit{normalized claims}. We propose \texttt{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, \texttt{CLAN}, comprising more than 6k instances of social media posts alongside their respective normalized claims. Experimentation demonstrates that \texttt{CACN} outperforms several baselines across various evaluation measures. A rigorous error analysis validates \texttt{CACN}‘s capabilities and pitfalls. We release our dataset and code at https://github.com/LCS2-IIITD/CACN-EMNLP-2023.