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Few-shot learning has drawn researchers' attention to overcome the problem of data scarcity.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
Earlier work this paper cites.
Adversarial attacks against fact extraction and verification
James Thorne and Andreas Vlachos. 2019 · 1903
Earlier work this paper cites.
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
Earlier work this paper cites.
Bert is not a knowledge base (yet): Factual knowledge vs. name-based reasoning in unsupervised qa
Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2019 · 1911
Earlier work this paper cites.
Caire-covid: A question answering and multi-document summarization system for covid-19 research
Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham J Barezi, and Pascale Fung. 2020 · 2005
Earlier work this paper cites.
Fully automated fact checking using external sources
Georgi Karadzhov, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, and Ivan Koychev. 2017 · 2017
Earlier work this paper cites.
Where the truth lies: Explaining the credibility of emerging claims on the web and social media
Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Where is your evidence: Improving fact-checking by justification modeling
Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018 · 2018
Earlier work this paper cites.
Credeye: A credibility lens for analyzing and explaining misinformation
Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2018a · 2018
Earlier work this paper cites.
DeClarE: Debunking fake news and false claims using evidence-aware deep learning
Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2018b · 2018
Earlier work this paper cites.
FEVER: a large-scale dataset for fact extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
Cited alongside, same era.
Tabfact: A large-scale dataset for table-based fact verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 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.
Overview of the clef-2019 checkthat! lab: Automatic identification and verification of claims. task 2: Evidence and factuality
Maram Hasanain, Reem Suwaileh, Tamer Elsayed, Alberto Barrón-Cedeno, and Preslav Nakov. 2019 · 2019
Cited alongside, same era.
Sentence-level evidence embedding for claim verification with hierarchical attention networks
Jing Ma, Wei Gao, Shafiq Joty, and Kam-Fai Wong. 2019 · 2019
Cited alongside, same era.
GEAR: Graph-based evidence aggregating and reasoning for fact verification
Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2019 · 2019
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TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
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DeSePtion: Dual sequence prediction and adversarial examples for improved fact-checking
Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, and Smaranda Muresan. 2020 · 2020
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Language models as fact checkers?
Nayeon Lee, Belinda Z. Li, Sinong Wang, Wen-tau Yih, Hao Ma, and Madian Khabsa. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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Combining fact extraction and verification with neural semantic matching networks
Yixin Nie, Haonan Chen, and Mohit Bansal. 2019 · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 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.
AttentiveChecker: A bi-directional attention flow mechanism for fact verification
Santosh Tokala, Vishal G, Avirup Saha, and Niloy Ganguly. 2019 · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Cited alongside, same era.
UCL machine reading group: Four factor framework for fact finding (HexaF)
Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018a
Cited in the paper.
UCL machine reading group: Four factor framework for fact finding (HexaF)
Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018b
Cited in the paper.
Later among the works it cites.
Language models as few-shot learner for task-oriented dialogue systems
Andrea Madotto, Zihan Liu, Zhaojiang Lin, and Pascale Fung. 2020 · 2020
Later among the works it cites.
How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
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Masked language model scoring
Julian Salazar, Davis Liang, Toan Q. Nguyen, and Katrin Kirchhoff. 2020 · 2020
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Evidence-aware hierarchical interactive attention networks for explainable claim verification
Lianwei Wu, Yuan Rao, Xiong Yang, Wanzhen Wang, and Ambreen Nazir. 2020 · 2020
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Reasoning over semantic-level graph for fact checking
Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2020 · 2020
Later among the works it cites.