Adversarial Training for Commonsense Inference

Roberta: A robustly optimized bert pretraining approach

Original

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019b · 1907

Earlier work this paper cites.

Smart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization

Original

Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao. 2019 · 1911

Earlier work this paper cites.

Virtual adversarial training: a regularization method for supervised and semi-supervised learning

Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018 · 1993

Earlier work this paper cites.

The microsoft toolkit of multi-task deep neural networks for natural language understanding

Original

Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, and Jianfeng Gao. 2020 · 2002

Earlier work this paper cites.

K-adapter: Infusing knowledge into pre-trained models with adapters

Original

Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Cuihong Cao, Daxin Jiang, Ming Zhou, et al. 2020 · 2002

Earlier work this paper cites.

Explaining and harnessing adversarial examples

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015

Earlier work this paper cites.

Adam: A method for stochastic optimization

Diederik P Kingma and Jimmy Ba. 2015 · 2015

Earlier work this paper cites.

Race: Large-scale reading comprehension dataset from examinations

Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017

Earlier work this paper cites.

Towards deep learning models resistant to adversarial attacks

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018

Cited alongside, same era.

Glue: A multi-task benchmark and analysis platform for natural language understanding

Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018

Cited alongside, same era.

Swag: A large-scale adversarial dataset for grounded commonsense inference

Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018

Cited alongside, same era.

Jeff da at coin-shared task

Jeff Da. 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.

Cosmos qa: Machine reading comprehension with contextual commonsense reasoning

Blcu-nlp at coin-shared task1: Stagewise fine-tuning bert for commonsense inference in everyday narrations

Chunhua Liu and Dong Yu. 2019 · 2019

Later among the works it cites.

Mcscript2. 0: A machine comprehension corpus focused on script events and participants

Simon Ostermann, Michael Roth, and Manfred Pinkal. 2019a · 2019

Later among the works it cites.

Adversarial robustness through local linearization

Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli, et al. 2019 · 2019

Later among the works it cites.

Iit-kgp at coin 2019: Using pre-trained language models for modeling machine comprehension

Prakhar Sharma and Sumegh Roychowdhury. 2019 · 2019

Later among the works it cites.

Xlnet: Generalized autoregressive pretraining for language understanding

Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019

Later among the works it cites.