  In this paper, we present coreference resolution experiments with a newly
created multilingual corpus CorefUD. We focus on the following languages:
Czech, Russian, Polish, German, Spanish, and Catalan. In addition to
monolingual experiments, we combine the training data in multilingual
experiments and train two joined models -- for Slavic languages and for all the
languages together. We rely on an end-to-end deep learning model that we
slightly adapted for the CorefUD corpus. Our results show that we can profit
from harmonized annotations, and using joined models helps significantly for
the languages with smaller training data.
