从混乱到清晰：赋能事实核查的声明规范化
From Chaos to Clarity: Claim Normalization to Empower Fact-Checking
AI总结：
本文提出声明规范化任务，旨在将嘈杂的社交媒体帖子分解为易懂的规范化声明，并构建数据集 CLAN；提出基于思维链和大模型上下文学习的 CACN 方法，在多项指标上超越基线。
摘要：
随着社交媒体的兴起，用户接触到许多误导性声明。然而，这些帖子中固有的普遍噪声给识别需要核实的精确且突出的声明带来了挑战。从这些帖子中提取重要声明既艰巨又耗时，但这仍是一个未充分探索的问题。在此，我们旨在填补这一空白。我们引入了一项新任务——声明规范化（Claim Normalization，简称 ClaimNorm），旨在将复杂且嘈杂的社交媒体帖子分解为更直接、更易懂的形式，即规范化声明。我们提出了 CACN，这是一种开创性的方法，利用思维链和声明值得核查性估计，模拟人类推理过程来理解复杂的声明。此外，我们利用大语言模型的上下文学习能力来提供指导并改进声明规范化。为评估所提模型的有效性，我们精心整理了一个综合的真实世界数据集 CLAN，包含超过 6k 条社交媒体帖子实例及其相应的规范化声明。实验表明，CACN 在多种评估指标上优于多个基线。最后，我们严格的错误分析验证了 CACN 的能力和缺陷。
英文摘要：
With the rise of social media, users are exposed to many misleading claims. However, the pervasive noise inherent in these posts presents a challenge in identifying precise and prominent claims that require verification. Extracting the important claims from such posts is arduous and time-consuming, yet it is an underexplored problem. Here, we aim to bridge this gap. We introduce a novel task, Claim Normalization (aka ClaimNorm), which 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 the in-context learning capabilities of large language models to provide guidance and to improve claim normalization. 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. Our experiments demonstrate that CACN outperforms several baselines across various evaluation measures. Finally, our rigorous error analysis validates CACN's capabilities and pitfalls.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
