BERT

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 36 articles )

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  1. Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, Rodrigo Nogueira: Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations (2021) arXiv
  2. Tao Gui, Xiao Wang, Qi Zhang, Qin Liu, Yicheng Zou, Xin Zhou, Rui Zheng, Chong Zhang, Qinzhuo Wu, Jiacheng Ye, Zexiong Pang, Yongxin Zhang, Zhengyan Li, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xinwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Bolin Zhu, Shan Qin, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang: TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021) arXiv
  3. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  4. 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)
  5. Frady, E. Paxon; Kent, Spencer J.; Olshausen, Bruno A.; Sommer, Friedrich T.: Resonator networks. I: An efficient solution for factoring high-dimensional, distributed representations of data structures (2020)
  6. 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)
  7. Het Shah, Avishree Khare, Neelay Shah, Khizir Siddiqui: KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization (2020) arXiv
  8. Jaap Jumelet: diagNNose: A Library for Neural Activation Analysis (2020) arXiv
  9. Jialun Cao, Meiziniu Li, Yeting Li, Ming Wen, Shing-Chi Cheung: SemMT: A Semantic-based Testing Approach for Machine Translation Systems (2020) arXiv
  10. Jipeng Qiang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu: LSBert: A Simple Framework for Lexical Simplification (2020) arXiv
  11. Kutlu, Mucahid; McDonnell, Tyler; Elsayed, Tamer; Lease, Matthew: Annotator rationales for labeling tasks in crowdsourcing (2020)
  12. Lee, Jaehoon; Xiao, Lechao; Schoenholz, Samuel S.; Bahri, Yasaman; Novak, Roman; Sohl-Dickstein, Jascha; Pennington, Jeffrey: Wide neural networks of any depth evolve as linear models under gradient descent (2020)
  13. Li, Dandan; Summers-Stay, Douglas: Dual embeddings and metrics for word and relational similarity (2020)
  14. 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
  15. Pieter Delobelle, Thomas Winters, Bettina Berendt: RobBERT: a Dutch RoBERTa-based Language Model (2020) arXiv
  16. Raeid Saqur, Ameet Deshpande: CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes (2020) arXiv
  17. 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
  18. Sai Muralidhar Jayanthi, Danish Pruthi, Graham Neubig: NeuSpell: A Neural Spelling Correction Toolkit (2020) arXiv
  19. Simpson, Edwin; Gurevych, Iryna: Scalable Bayesian preference learning for crowds (2020)
  20. Subhabrata Mukherjee, Ahmed Awadallah: TinyMBERT: Multi-Stage Distillation Framework for Massive Multi-lingual NER (2020) arXiv

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