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Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 
Added by: SijanLibrarian (2020-10-28 16:45:46)   Last edited by: SijanLibrarian (2020-10-28 16:47:50)
Resource type: Journal Article
BibTeX citation key: Devlin2018
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Categories: Artificial Intelligence, Computer Science, Data Sciences, Decision Theory, General, Innovation, Mathematics, Military Science
Subcategories: Big data, Cognitive Electronic Warfare, Command and control, Cyber, Deep learning, Forecasting, Informatics, Machine learning, Machine recognition, Markov models, Q-learning
Creators: Chang, Devlin, Lee, Toutanova
Collection: arXiv preprint arXiv:1810.04805
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Abstract
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, 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).
  
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