  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.
