Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019, June). Bert: 
Pre-training of deep bidirectional transformers for language 
understanding. In Proceedings of the 2019 conference of the North 
American chapter of the association for computational linguistics: 
human language technologies, volume 1 (long and short papers) 
(pp. 4171-4186).

Abstract
We introduce a new language representation model called BERT, 
which stands for Bidirectional Encoder Representations from 
Transformers. Unlike recent language representation models (Peters et 
al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep 
bidirectional representations from unlabeled text by jointly conditioning 
on both left and right context in all layers. As a result, the pre-trained 
BERT model can be fine-tuned with just one additional output layer to 
create state-of-the-art models for a wide range of tasks, such as question 
answering and language inference, without substantial task-specific 
architecture modifications. BERT is conceptually simple and empirically 
powerful. It obtains new state-of-the-art results on eleven natural 
language processing tasks, including pushing the GLUE score to 80.5 
(7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% 
absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 
(1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 
point absolute improvement).