  In this work, we introduce a corpus for satire detection in Romanian news. We
gathered 55,608 public news articles from multiple real and satirical news
sources, composing one of the largest corpora for satire detection regardless
of language and the only one for the Romanian language. We provide an official
split of the text samples, such that training news articles belong to different
sources than test news articles, thus ensuring that models do not achieve high
performance simply due to overfitting. We conduct experiments with two
state-of-the-art deep neural models, resulting in a set of strong baselines for
our novel corpus. Our results show that the machine-level accuracy for satire
detection in Romanian is quite low (under 73% on the test set) compared to the
human-level accuracy (87%), leaving enough room for improvement in future
research.
