Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text

Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text Lukas Lange author Anastasiia Iurshina author Heike Adel author Jannik Strötgen author 2020-jul text Proceedings of the 5th Workshop on Representation Learning for NLP Association for Computational Linguistics Online conference publication 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. lange-etal-2020-adversarial https://www.aclweb.org/anthology/2020.repl4nlp-1.14 2020-jul 103 109