  Even though SRL is researched for many languages, major improvements have
mostly been obtained for English, for which more resources are available. In
fact, existing multilingual SRL datasets contain disparate annotation styles or
come from different domains, hampering generalization in multilingual learning.
In this work, we propose a method to automatically construct an SRL corpus that
is parallel in four languages: English, French, German, Spanish, with unified
predicate and role annotations that are fully comparable across languages. We
apply high-quality machine translation to the English CoNLL-09 dataset and use
multilingual BERT to project its high-quality annotations to the target
languages. We include human-validated test sets that we use to measure the
projection quality, and show that projection is denser and more precise than a
strong baseline. Finally, we train different SOTA models on our novel corpus
for mono- and multilingual SRL, showing that the multilingual annotations
improve performance especially for the weaker languages.
