  This paper presents an overview of the shared task on multilingual
coreference resolution associated with the CRAC 2022 workshop. Shared task
participants were supposed to develop trainable systems capable of identifying
mentions and clustering them according to identity coreference. The public
edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used
as the source of training and evaluation data. The CoNLL score used in previous
coreference-oriented shared tasks was used as the main evaluation metric. There
were 8 coreference prediction systems submitted by 5 participating teams; in
addition, there was a competitive Transformer-based baseline system provided by
the organizers at the beginning of the shared task. The winner system
outperformed the baseline by 12 percentage points (in terms of the CoNLL scores
averaged across all datasets for individual languages).
