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Recent advances, such as GPT and BERT, have shown success in incorporating a pre-trained transformer language model and fine-tuning operation to improve downstream NLP systems.
A survey on transfer learning
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Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
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Learning deep structured semantic models for web search using clickthrough data
Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck · 2013
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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
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Representation learning using multi-task deep neural networks for semantic classification and information retrieval
Xiaodong Liu, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, and Ye-yi Wang · 2015
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A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen · 2016
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Pay attention to the ending: Strong neural baselines for the roc story cloze task
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Snigdha Chaturvedi, Haoruo Peng, and Dan Roth · 2017
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Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A Smith · 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
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Conditional generative adversarial networks for commonsense machine comprehension
Bingning Wang, Kang Liu, and Jun Zhao · 2017
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Qian Li, Ziwei Li, Jin-Mao Wei, Yanhui Gu, Adam Jatowt, and Zhenglu Yang · 2018
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Jason Phang, Thibault Févry, and Samuel R Bowman · 2018
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Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Tackling the story ending biases in the story cloze test
Rishi Sharma, James Allen, Omid Bakhshandeh, and Nasrin Mostafazadeh · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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Constructing narrative event evolutionary graph for script event prediction
Zhongyang Li, Xiao Ding, and Ting Liu · 2018
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Generating reasonable and diversified story ending using sequence to sequence model with adversarial training
Zhongyang Li, Xiao Ding, and Ting Liu · 2018
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Personalizing a dialogue system with transfer reinforcement learning
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi · 2018
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Incorporating structured commonsense knowledge in story completion
Jiaao Chen, Jianshu Chen, and Zhou Yu · 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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Story ending selection by finding hints from pairwise candidate endings
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