论文标题
多语言模型的对抗性对齐，用于从文本中提取时间表达式
Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text
论文作者
论文摘要
尽管时间标记仍由基于规则的系统主导，但最近在神经时间标记器上尝试了。但是，所有人都专注于单语设置。在本文中，我们探讨了从文本中提取时间表达式的多语言方法，并研究了将嵌入空间对准一个公共空间的对抗训练。这样，我们创建了一个多语言模型，该模型也可以转移到看不见的语言，并在那些跨语性转移实验中设置新的最新技术。
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.
