  Although temporal tagging is still dominated by rule-based systems, there
have been recent attempts at neural temporal taggers. However, all of them
focus on monolingual settings. In this paper, we explore multilingual methods
for the extraction of temporal expressions from text and investigate
adversarial training for aligning embedding spaces to one common space. With
this, we create a single multilingual model that can also be transferred to
unseen languages and set the new state of the art in those cross-lingual
transfer experiments.
