BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 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).

References in zbMATH (referenced in 29 articles )

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  1. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  2. Bullock, Joseph; Luccioni, Alexandra; Pham, Katherine Hoffman; Lam, Cynthia Sin Nga; Luengo-Oroz, Miguel: Mapping the landscape of artificial intelligence applications against COVID-19 (2020)
  3. Guo, Jian; He, He; He, Tong; Lausen, Leonard; Li, Mu; Lin, Haibin; Shi, Xingjian; Wang, Chenguang; Xie, Junyuan; Zha, Sheng; Zhang, Aston; Zhang, Hang; Zhang, Zhi; Zhang, Zhongyue; Zheng, Shuai; Zhu, Yi: GluonCV and GluonNLP: deep learning in computer vision and natural language processing (2020)
  4. Het Shah, Avishree Khare, Neelay Shah, Khizir Siddiqui: KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization (2020) arXiv
  5. Jaap Jumelet: diagNNose: A Library for Neural Activation Analysis (2020) arXiv
  6. Jialun Cao, Meiziniu Li, Yeting Li, Ming Wen, Shing-Chi Cheung: SemMT: A Semantic-based Testing Approach for Machine Translation Systems (2020) arXiv
  7. Jipeng Qiang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu: LSBert: A Simple Framework for Lexical Simplification (2020) arXiv
  8. Kutlu, Mucahid; McDonnell, Tyler; Elsayed, Tamer; Lease, Matthew: Annotator rationales for labeling tasks in crowdsourcing (2020)
  9. Li, Dandan; Summers-Stay, Douglas: Dual embeddings and metrics for word and relational similarity (2020)
  10. Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi Inaguma, Naoyuki Kamo, Chenda Li, Daniel Garcia-Romero, Jiatong Shi, Jing Shi, Shinji Watanabe, Kun Wei, Wangyou Zhang, Yuekai Zhang: Recent Developments on ESPnet Toolkit Boosted by Conformer (2020) arXiv
  11. Raeid Saqur, Ameet Deshpande: CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes (2020) arXiv
  12. Rob van der Goot, Ahmet Üstün, Alan Ramponi, Barbara Plank: Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP (2020) arXiv
  13. Sai Muralidhar Jayanthi, Danish Pruthi, Graham Neubig: NeuSpell: A Neural Spelling Correction Toolkit (2020) arXiv
  14. Simpson, Edwin; Gurevych, Iryna: Scalable Bayesian preference learning for crowds (2020)
  15. Subhabrata Mukherjee, Ahmed Awadallah: TinyMBERT: Multi-Stage Distillation Framework for Massive Multi-lingual NER (2020) arXiv
  16. Tang, Meng; Liu, Yimin; Durlofsky, Louis J.: A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems (2020)
  17. Tikhomirov, M. M.; Loukachevitch, N. V.; Dobrov, B. V.: Recognizing named entities in specific domain (2020)
  18. Uma Roy, Noah Constant, Rami Al-Rfou, Aditya Barua, Aaron Phillips, Yinfei Yang: LAReQA: Language-agnostic answer retrieval from a multilingual pool (2020) arXiv
  19. Victor Dibia: NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2020) arXiv
  20. Xiaohui Wang, Ying Xiong, Yang Wei, Mingxuan Wang, Lei Li: LightSeq: A High Performance Inference Library for Sequence Processing and Generation (2020) arXiv

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