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Although temporal tagging is still dominated by rule-based systems, therehave been recent attempts at neural temporal taggers. However, all of themfocus on monolingual settings. In this paper, we explore multilingual methodsfor the extraction of temporal expressions from text and investigateadversarial training for aligning embedding spaces to one common space. Withthis, we create a single multilingual model that can also be transferred tounseen languages and set the new state of the art in those cross-lingualtransfer experiments.

Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text