论文标题
阿拉伯语的神经核心分辨率
Neural Coreference Resolution for Arabic
论文作者
论文摘要
没有针对阿拉伯语的神经核心排列器，实际上，自从（Bjorkelund and Kuhn，2014年）以来，我们并不知道阿拉伯语的任何基于学习的核心排位器。在本文中，我们基于Lee等人的端到端体系结构，与阿拉伯语版本的BERT和外部提及检测器一起介绍了阿拉伯语的核心分辨率系统。据我们所知，这是专门针对阿拉伯语的第一个神经核心分辨率系统，并且在Ontonotes 5.0上的现有最新状态优于15.2分Conll F1。我们还讨论了阿拉伯语和可能应对这些挑战的可能方法的当前局限性。
No neural coreference resolver for Arabic exists, in fact we are not aware of any learning-based coreference resolver for Arabic since (Bjorkelund and Kuhn, 2014). In this paper, we introduce a coreference resolution system for Arabic based on Lee et al's end to end architecture combined with the Arabic version of bert and an external mention detector. As far as we know, this is the first neural coreference resolution system aimed specifically to Arabic, and it substantially outperforms the existing state of the art on OntoNotes 5.0 with a gain of 15.2 points conll F1. We also discuss the current limitations of the task for Arabic and possible approaches that can tackle these challenges.
