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Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 1906
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Sensebert: Driving some sense into bert
Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, and Yoav Shoham. 2019 · 1908
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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 · 1909
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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. 2019 · 1910
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Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2009 · 1911
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Text mining for causal relations
Roxana Girju and Dan I. Moldovan. 2002 · 2002
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K-adapter: Infusing knowledge into pre-trained models with adapters
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Cuihong Cao, Daxin Jiang, and Ming Zhou. 2020 · 2002
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Recall and learn: Fine-tuning deep pretrained language models with less forgetting
Shou Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, and Xiang-Zhan Yu. 2020 · 2004
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Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2004
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Temporal common sense acquisition with minimal supervision
Ben Zhou, Qiang Ning, Daniel Khashabi, and Dan Roth. 2020 · 2005
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Commonsense causal reasoning using millions of personal stories
A. S Gordon, C. A Bejan, and K. Sagae. 2011 · 2011
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
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Utdhlt: Copacetic system for choosing plausible alternatives
Travis Goodwin, Bryan Rink, Kirk Roberts, and Sanda M. Harabagiu. 2012 · 2012
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N-gram counts and language models from the common crawl
C. Buck, K. Heafield, and B. van Ooyen. 2014 · 2014
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Annotating causality in the tempeval-3 corpus
Paramita Mirza, Rachele Sprugnoli, Sara Tonelli, and Manuela Speranza. 2014 · 2014
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Commonsense causal reasoning between short texts
Zhiyi Luo, Yuchen Sha, Kenny Q. Zhu, Seung won Hwang, and Zhongyuan Wang. 2016 · 2016
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Creating causal embeddings for question answering with minimal supervision
Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Peter Clark, and Michael Hammond. 2016 · 2016
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2016 · 2016
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Answering binary causal questions through large-scale text mining: An evaluation using cause-effect pairs from human experts
Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, and Michael Katz. 2019 · 2019
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Cosmos qa: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 2019
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When choosing plausible alternatives, clever hans can be clever
Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert, and Kentaro Inui. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Overcoming catastrophic forgetting in neural networks
James N Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. 2017 · 2017
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Handling multiword expressions in causality estimation
Shota Sasaki, Sho Takase, Naoya Inoue, Naoaki Okazaki, and Kentaro Inui. 2017 · 2017
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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Constructing narrative event evolutionary graph for script event prediction
Zhongyang Li, Xiao Ding, and Ting Liu. 2018 · 2018
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Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R Bowman. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Cited alongside, same era.
DisSent: Learning sentence representations from explicit discourse relations
A. Nie, E. Bennett, and N. Goodman. 2019 · 2019
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Knowledge enhanced contextual word representations
Matthew E. Peters, Mark Neumann, IV RobertLLogan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. 2019 · 2019
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Ibm scenario planning advisor: Plan recognition as ai planning in practice
Shirin Sohrabi, Michael Katz, Oktie Hassanzadeh, Octavian Udrea, Mark D. Feblowitz, and Anton Riabov. 2019 · 2019
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Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Yongqin Xian, Christoph H. Lampert, Bernt Schiele, and Zeynep Akata. 2019 · 2019
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Distributed representation of words in cause and effect spaces
Zhipeng Xie and Feiteng Mu. 2019 · 2019
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Ernie: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
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Guided generation of cause and effect
Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, and Benjamin Van Durme. 2020 · 2020
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