  We describe the winning submission to the CRAC 2022 Shared Task on
Multilingual Coreference Resolution. Our system first solves mention detection
and then coreference linking on the retrieved spans with an
antecedent-maximization approach, and both tasks are fine-tuned jointly with
shared Transformer weights. We report results of fine-tuning a wide range of
pretrained models. The center of this contribution are fine-tuned multilingual
models. We found one large multilingual model with sufficiently large encoder
to increase performance on all datasets across the board, with the benefit not
limited only to the underrepresented languages or groups of typologically
relative languages. The source code is available at
https://github.com/ufal/crac2022-corpipe.
