text: Dan Kondratyuk and Milan Straka. 2019. 75 Lan- 
  guages, 1 Model: Parsing Universal Dependen- 
  In Proceedings of the 2019 
  cies Universally. 
  Conference on Empirical Methods in Natural 
  Language Processing and the 9th International 
  Joint Conference on Natural Language Process- 
  ing (EMNLP-IJCNLP), pages 2779–2795, Hong 
  Kong, China. Association for Computational 
  Linguistics. 
  
  Haoming Jiang, Pengcheng He, Weizhu Chen, Xi- 
  aodong Liu, Jianfeng Gao, and Tuo Zhao. 2019a. 
  SMART: Robust and Efﬁcient Fine-Tuning for 
  Pre-trained Natural Language Models through 
  arXiv 
  Principled Regularized Optimization. 
  preprint arXiv:1911.03437.  Lingpeng Kong, Cyprien de Masson d’Autume, Lei 
  Yu, Wang Ling, Zihang Dai, and Dani Yogatama. 
  2019. A mutual information maximization per- 
  spective of language representation learning. In 