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Transformer is the backbone of modern NLP models.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 1904
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RoBERTa: A robustly optimized BERT pretraining approach
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Megatron-LM: Training multi-billion parameter language models using gpu model parallelism
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Towards a human-like open-domain chatbot
Daniel Adiwardana, Minh-Thang Luong, David R So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, et al. 2020 · 2001
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Efficient content-based sparse attention with routing transformers
Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier. 2020 · 2003
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2004
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Li, Madian Khabsa, Han Fang, and Hao Ma. 2020 · 2006
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Hopfield networks is all you need
Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Milena Pavlović, Geir Kjetil Sandve, Victor Greiff, et al. 2020 · 2008
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler. 2020 · 2009
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Fixup initialization: Residual learning without normalization
Hongyi Zhang, Yann N Dauphin, and Tengyu Ma. 2018 · 2018
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Transformer-XL: Attentive language models beyond a fixed-length context
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Improving deep transformer with depth-scaled initialization and merged attention
Biao Zhang, Ivan Titov, and Rico Sennrich. 2019 · 2019
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ETC: Encoding long and structured inputs in transformers
Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, and Li Yang. 2020 · 2020
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ELECTRA: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020 · 2020
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Improving transformer optimization through better initialization
Xiao Shi Huang, Felipe Perez, Jimmy Ba, and Maksims Volkovs. 2020 · 2020
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Reformer: The efficient transformer
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Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
Cited alongside, same era.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Cited alongside, same era.
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Open domain web keyphrase extraction beyond language modeling
Lee Xiong, Chuan Hu, Chenyan Xiong, Daniel Campos, and Arnold Overwijk. 2019 · 2019
Cited alongside, same era.
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya. 2020 · 2020
Closest in time.
Understanding the difficulty of training transformers
Liyuan Liu, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, and Jiawei Han. 2020 · 2020
Closest in time.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Closest in time.
ERNIE 2.0: A continual pre-training framework for language understanding
Yu Sun, Shuohuan Wang, Yu-Kun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. 2020 · 2020
Closest in time.
On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu. 2020 · 2020
Closest in time.
Big Bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. 2020 · 2020
Closest in time.