SemEval-2025 任务7：多语言与跨语言事实核查声明检索
SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
AI总结：
针对虚假信息研究中多语言及低资源语言被忽视的问题，SemEval-2025任务7开展多/跨语言事实核查声明检索共享任务，设单语、跨语两个子赛道，吸引179人参与并提交52份测试结果，相关成果为该领域研究提供参考。
摘要：
在线虚假信息的快速传播是一项全球性挑战，机器学习作为潜在解决方案已被广泛研究。然而，该领域往往忽视多语言场景和低资源语言。为弥补这一空白，我们在SemEval 2025上组织了一项多语言声明检索共享任务，旨在识别与不同语言的社交媒体帖子中新出现的声明相匹配的已事实核查声明。该任务包含两个子赛道：（1）单语言赛道，社交帖子与声明使用相同语言；（2）跨语言赛道，社交帖子与声明可能使用不同语言。共有179名参与者注册该任务，提交了52份测试结果。31支队伍中有23支提交了系统论文。本文报告了两个子赛道中性能最优的系统，以及最常见和最有效的方法。这项共享任务及其数据集和参赛系统为多语言声明检索和自动事实核查提供了宝贵见解，为该领域的未来研究提供支持。
英文摘要：
The rapid spread of online disinformation presents a global challenge, and machine learning has been widely explored as a potential solution. However, multilingual settings and low-resource languages are often neglected in this field. To address this gap, we conducted a shared task on multilingual claim retrieval at SemEval 2025, aimed at identifying fact-checked claims that match newly encountered claims expressed in social media posts across different languages. The task includes two subtracks: (1) a monolingual track, where social posts and claims are in the same language, and (2) a crosslingual track, where social posts and claims might be in different languages. A total of 179 participants registered for the task contributing to 52 test submissions. 23 out of 31 teams have submitted their system papers. In this paper, we report the best-performing systems as well as the most common and the most effective approaches across both subtracks. This shared task, along with its dataset and participating systems, provides valuable insights into multilingual claim retrieval and automated fact-checking, supporting future research in this field.
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。
