打破语言障碍：MMTweets推动跨语言已辟谣叙事检索以支持事实核查
Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking
AI总结：
本研究创建MMTweets数据集，引入跨语言已辟谣叙事检索任务，通过多阶段检索方法提升跨语言事实核查效率，并公开数据集与标注手册。
摘要：
查找先前已辟谣的叙事涉及识别已经过事实核查的声明。当类似的虚假声明在多种语言中持续存在，尽管辟谣信息已在另一种语言中可用数月，问题会加剧。因此，自动在多种语言中查找辟谣（或事实核查）对于充分利用稀缺的事实核查资源至关重要。主要由于缺乏现成数据，这一问题研究不足，尤其是在跨语言场景下，即检索与所核查在线帖子语言不同的辟谣信息。本研究引入跨语言已辟谣叙事检索，并通过以下方式填补这一研究空白：（i）创建多语言错误信息推文（MMTweets）数据集：该数据集具有跨语言配对、图像、人工标注和细粒度标签，相比同类数据集更为全面；（ii）进行广泛实验，以基准测试最先进的跨语言检索模型，并引入针对该任务设计的多阶段检索方法；（iii）全面评估检索模型在MMTweets内的跨语言和跨数据集迁移能力，并进行检索延迟分析。我们发现MMTweets为跨语言已辟谣叙事检索带来挑战，凸显了检索模型需改进之处。尽管如此，本研究为创建MMTweets数据集和优化已辟谣叙事检索模型以赋能事实核查工作提供了宝贵见解。数据集和标注编码手册公开可用，网址为https://doi.org/10.5281/zenodo.10637161。
英文摘要：
Finding previously debunked narratives involves identifying claims that have already undergone fact-checking. The issue intensifies when similar false claims persist in multiple languages, despite the availability of debunks for several months in another language. Hence, automatically finding debunks (or fact-checks) in multiple languages is crucial to make the best use of scarce fact-checkers' resources. Mainly due to the lack of readily available data, this is an understudied problem, particularly when considering the cross-lingual scenario, i.e. the retrieval of debunks in a language different from the language of the online post being checked. This study introduces cross-lingual debunked narrative retrieval and addresses this research gap by: (i) creating Multilingual Misinformation Tweets (MMTweets): a dataset that stands out, featuring cross-lingual pairs, images, human annotations, and fine-grained labels, making it a comprehensive resource compared to its counterparts; (ii) conducting an extensive experiment to benchmark state-of-the-art cross-lingual retrieval models and introducing multistage retrieval methods tailored for the task; and (iii) comprehensively evaluating retrieval models for their cross-lingual and cross-dataset transfer capabilities within MMTweets, and conducting a retrieval latency analysis. We find that MMTweets presents challenges for cross-lingual debunked narrative retrieval, highlighting areas for improvement in retrieval models. Nonetheless, the study provides valuable insights for creating MMTweets datasets and optimising debunked narrative retrieval models to empower fact-checking endeavours. The dataset and annotation codebook are publicly available at https://doi.org/10.5281/zenodo.10637161.
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。
