text: Data. In Proceedings of the 58th Annual Meet- 
  ing of the Association for Computational Lin- 
  guistics, pages 8413–8426, Online. Association 
  for Computational Linguistics. 
  
  Yiyun Zhao and Steven Bethard. 2020. How does 
  BERT’s attention change when you ﬁne-tune? 
  An analysis methodology and a case study in 
  negation scope. In Proceedings of the 58th An- 
  nual Meeting of the Association for Computa- 
  tional Linguistics, pages 4729–4747, Online. As- 
  sociation for Computational Linguistics. 
  
  Dani Yogatama, Cyprien de Masson d’Autume, 
  Jerome Connor, Tomas Kocisky, Mike 
  Chrzanowski, Lingpeng Kong, Angeliki Lazari- 
  dou, Wang Ling, Lei Yu, Chris Dyer, and Phil 
  Blunsom. 2019. Learning and Evaluating Gen- 
  eral Linguistic Intelligence. arXiv:1901.11373 
  [cs, stat]. 
  
  Wenxuan Zhou, 
  
  Junyi Du, and Xiang Ren. 
  Improving BERT Fine-tuning with 
  arXiv preprint 
  
  2019. 
  Embedding Normalization. 
  arXiv:1911.03918. 