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The introduction of pre-trained language models has revolutionized natural language research communities.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder · 2003
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Automatically constructing a corpus of sentential paraphrases
William B Dolan and Chris Brockett · 2005
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Gregory S Corrado, and Jeffrey Dean · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Montreal neural machine translation systems for wmt’15
Sébastien Jean, Orhan Firat, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio · 2015
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Gated feedback recurrent neural networks
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Multi-view recurrent neural acoustic word embeddings
Wanjia He, Weiran Wang, and Karen Livescu · 2016
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Named entity recognition with bidirectional lstm-cnns
Jason PC Chiu and Eric Nichols · 2016
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer · 2016
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End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard Hovy · 2016
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Highway long short-term memory rnns for distant speech recognition
Yu Zhang, Guoguo Chen, Dong Yu, Kaisheng Yaco, Sanjeev Khudanpur, and James Glass · 2016
Cited alongside, same era.
Siamese recurrent architectures for learning sentence similarity
Jonas Mueller and Aditya Thyagarajan · 2016
Cited alongside, same era.
A compare-aggregate model for matching text sequences
Shuohang Wang and Jing Jiang · 2016
Cited alongside, same era.
Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen · 2016
Cited alongside, same era.
Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher · 2017
Cited alongside, same era.
Semi-supervised sequence modeling with cross-view training
Kevin Clark, Minh-Thang Luong, Christopher D Manning, and Quoc V Le · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher G Clark, Kenton Lee, and Luke S Zettlemoyer · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
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Do convolutional networks need to be deep for text classification?
Hoa T Le, Christophe Cerisara, and Alexandre Denis · 2018
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Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
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To tune or not to tune? adapting pretrained representations to diverse tasks
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
An in-depth look at google’s first tensor processing unit (tpu)
Kaz Sato, Cliff Young, and David Patterson · 2017
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2017
Cited alongside, same era.
Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia · 2017
Cited alongside, same era.
Bilateral multi-perspective matching for natural language sentences
Zhiguo Wang, Wael Hamza, and Radu Florian · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning · 2018
Cited alongside, same era.
Matthew Peters, Sebastian Ruder, and Noah A Smith · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Ernie: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu · 2019
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Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao · 2019
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Linguistic knowledge and transferability of contextual representations
Nelson F Liu, Matt Gardner, Yonatan Belinkov, Matthew Peters, and Noah A Smith · 2019
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Bert and pals: Projected attention layers for efficient adaptation in multi-task learning
Asa Cooper Stickland and Iain Murray · 2019
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Bert post-training for review reading comprehension and aspect-based sentiment analysis
Hu Xu, Bing Liu, Lei Shu, and Philip S Yu · 2019
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How to fine-tune bert for text classification?
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang · 2019
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