QCRI 参与 SemEval-2023 任务3：使用多语言模型检测新闻体裁、框架构建与说服技巧
QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models
AI总结：
针对网络错误信息人工核查难的问题，本文介绍了QCRI团队参与SemEval-2023任务3的多语言新闻体裁、框架与说服技巧检测系统，该系统在27个测试设置中有10个进入前三。
摘要：
主流媒体和社交媒体上传播的错误信息一直在以各种方式误导用户。记者和事实核查人员的人工检测与核实工作已无法应对误导性信息的庞大规模与快速传播。这推动了研究界和业界致力于开发用于分析和核实网络传播新闻的系统。SemEval-2023 任务3旨在解决这一核心问题下的多个子任务，聚焦新闻文章中用于影响读者观点的写作技巧。该任务包含3个子任务，涉及6种语言，另有3种“惊喜”测试语言，共形成27种不同的测试设置。本文介绍了我们参与该任务的系统。我们团队是成功提交所有测试设置运行结果的6支队伍之一。官方结果显示，在27种测试设置中，我们的系统有10种跻身前三。
英文摘要：
Misinformation spreading in mainstream and social media has been misleading users in different ways. Manual detection and verification efforts by journalists and fact-checkers can no longer cope with the great scale and quick spread of misleading information. This motivated research and industry efforts to develop systems for analyzing and verifying news spreading online. The SemEval-2023 Task 3 is an attempt to address several subtasks under this overarching problem, targeting writing techniques used in news articles to affect readers' opinions. The task addressed three subtasks with six languages, in addition to three ``surprise'' test languages, resulting in 27 different test setups. This paper describes our participating system to this task. Our team is one of the 6 teams that successfully submitted runs for all setups. The official results show that our system is ranked among the top 3 systems for 10 out of the 27 setups.
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。
