Fact-checkers are often hampered by the sheer amount of online content that
needs to be fact-checked. NLP can help them by retrieving already existing
fact-checks relevant to the content being investigated. This paper introduces a
new multilingual dataset -- MultiClaim -- for previously fact-checked claim
retrieval. We collected 28k posts in 27 languages from social media, 206k
fact-checks in 39 languages written by professional fact-checkers, as well as
31k connections between these two groups. This is the most extensive and the
most linguistically diverse dataset of this kind to date. We evaluated how
different unsupervised methods fare on this dataset and its various dimensions.
We show that evaluating such a diverse dataset has its complexities and proper
care needs to be taken before interpreting the results. We also evaluated a
supervised fine-tuning approach, improving upon the unsupervised method
significantly.

本论文提出了一个新的多语言数据集 MultiClaim，其中包含 28k 个社交媒体帖子和 206k 个来自专业事实核查人员写的 39 种语言的事实核查。我们评估了不同的非监督方法在这个数据集上的效果，并显示对这样一个多样化的数据集进行评估具有复杂性，需要在解释结果之前采取适当的措施。我们还评估了一种监督微调方法，显著提高了非监督方法的性能。