  The detection and normalization of temporal expressions is an important task
and preprocessing step for many applications. However, prior work on
normalization is rule-based, which severely limits the applicability in
real-world multilingual settings, due to the costly creation of new rules. We
propose a novel neural method for normalizing temporal expressions based on
masked language modeling. Our multilingual method outperforms prior rule-based
systems in many languages, and in particular, for low-resource languages with
performance improvements of up to 33 F1 on average compared to the state of the
art.
