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 58 articles )

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  1. Bakhtin, Anton; Deng, Yuntian; Gross, Sam; Ott, Myle; Ranzato, Marc’aurelio; Szlam, Arthur: Residual energy-based models for text (2021)
  2. Baskerville, Nicholas P.; Keating, Jonathan P.; Mezzadri, Francesco; Najnudel, Joseph: The loss surfaces of neural networks with general activation functions (2021)
  3. Christopher Schröder, Lydia Müller, Andreas Niekler, Martin Potthast: Small-text: Active Learning for Text Classification in Python (2021) arXiv
  4. Fresca, Stefania; Dede’, Luca; Manzoni, Andrea: A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (2021)
  5. Ghukasyan, Tsolak; Yeshilbashyan, Yeva; Avetisyan, Karen: Subwords-only alternatives to fasttext for morphologically rich languages (2021)
  6. Hitoshi Manabe, Masato Hagiwara: EXPATS: A Toolkit for Explainable Automated Text Scoring (2021) arXiv
  7. Hoherchak, H.; Darchuk, N.; Kryvyi, S.: Representation, analysis, and extraction of knowledge from unstructured natural language texts (2021)
  8. 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
  9. Jones, Ilenna Simone; Kording, Konrad Paul: Might a single neuron solve interesting machine learning problems through successive computations on its dendritic tree? (2021)
  10. Juan Manuel Pérez, Juan Carlos Giudici, Franco Luque: pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks (2021) arXiv
  11. Koyyalagunta, Divya; Sun, Anna; Draelos, Rachel Lea; Rudin, Cynthia: Playing codenames with language graphs and word embeddings (2021)
  12. Lukas Stappen, Lea Schumann, Benjamin Sertolli, Alice Baird, Benjamin Weigel, Erik Cambria, Björn W. Schuller: MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox (2021) arXiv
  13. Mehdi Bahrami, N.C. Shrikanth, Shade Ruangwan, Lei Liu, Yuji Mizobuchi, Masahiro Fukuyori, Wei-Peng Chen, Kazuki Munakata, Tim Menzies: PyTorrent: A Python Library Corpus for Large-scale Language Models (2021) arXiv
  14. Mingxiang Chen, Zhanguo Chang, Haonan Lu, Bitao Yang, Zhuang Li, Liufang Guo, Zhecheng Wang: AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation (2021) arXiv
  15. Pérez, Jorge; Barceló, Pablo; Marinkovic, Javier: Attention is Turing-complete (2021)
  16. Sai Muralidhar Jayanthi, Kavya Nerella, Khyathi Raghavi Chandu, Alan W Black: CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing (2021) arXiv
  17. Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, Shujie Liu: CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021) arXiv
  18. 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
  19. Tarau, Paul; Blanco, Eduardo: Interactive text graph mining with a prolog-based dialog engine (2021)
  20. Volpi, Riccardo; Malagò, Luigi: Natural alpha embeddings (2021)

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