论文标题
Clef Checkthat的Alex团队！ 2020：通过变压器模型识别值得核对的推文
Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models
论文作者
论文摘要
尽管错误的信息和虚假信息在社交媒体上已经蓬勃发展了多年，但随着19日大流行的出现，政治和健康错误信息汇合了，从而将问题提升到了一个全新的水平，并引起了第一个全球不足的流行病。与这种不相容的斗争具有许多方面，事实核对和揭穿了错误和误导性的主张是最重要的主张。不幸的是，手动事实检查是耗时的，自动事实检查是资源强度的，这意味着我们需要对输入社交媒体帖子进行过滤，并抛出那些似乎不值得检查的帖子。考虑到这一点，我们在这里提出了一个模型，用于检测有关COVID-19的值得检查的推文，该推文结合了深层上下文化的文本表示与建模推文的社交环境。我们进一步描述了许多其他实验和比较，我们认为这对于将来的研究应该很有用，因为它们可以表明哪种技术对任务有效。我们正式提交给英文版本的Clef-2020 Checkthat！任务1 System team_alex以0.8034的地图得分排名第二，这几乎与胜利系统息息相关，仅落后于0.003 MAP点绝对。
While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic. The fight against this infodemic has many aspects, with fact-checking and debunking false and misleading claims being among the most important ones. Unfortunately, manual fact-checking is time-consuming and automatic fact-checking is resource-intense, which means that we need to pre-filter the input social media posts and to throw out those that do not appear to be check-worthy. With this in mind, here we propose a model for detecting check-worthy tweets about COVID-19, which combines deep contextualized text representations with modeling the social context of the tweet. We further describe a number of additional experiments and comparisons, which we believe should be useful for future research as they provide some indication about what techniques are effective for the task. Our official submission to the English version of CLEF-2020 CheckThat! Task 1, system Team_Alex, was ranked second with a MAP score of 0.8034, which is almost tied with the wining system, lagging behind by just 0.003 MAP points absolute.
