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Natural language understanding involves reading between the lines with implicit background knowledge.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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Xlnet: Generalized autoregressive pretraining for language understanding
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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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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Dynamic knowledge graph construction for zero-shot commonsense question answering
Antoine Bosselut and Yejin Choi. 2019 · 1911
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Negated lama: Birds cannot fly
Nora Kassner and Hinrich Schütze. 2019 · 1911
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Learning to answer by learning to ask: Getting the best of gpt-2 and bert worlds
Tassilo Klein and Moin Nabi. 2019 · 1911
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Harvesting paragraph-level question-answer pairs from Wikipedia
Xinya Du and Claire Cardie. 2018 · 1917
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The act of discovery
Jerome S Bruner. 1961 · 1961
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The measurement of observer agreement for categorical data
J Richard Landis and Gary G Koch. 1977 · 1977
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Deep read: A reading comprehension system
Lynette Hirschman, Marc Light, Eric Breck, and John D Burger. 1999 · 1999
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Contextualized representations using textual encyclopedic knowledge
Mandar Joshi, Kenton Lee, Yi Luan, and Kristina Toutanova. 2020 · 2004
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Explaining question answering models through text generation
Veronica Latcinnik and Jonathan Berant. 2020 · 2004
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Web 1t 5-gram version 1 (2006)
Thorsten Brants and Alex Franz. 2006 · 2006
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The Importance of Being Important: Question Generation
Lucy Vanderwende. 2008 · 2008
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SemEval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Andrew Gordon, Zornitsa Kozareva, and Melissa Roemmele. 2012 · 2012
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The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
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Representing general relational knowledge in conceptnet 5
Robyn Speer and Catherine Havasi. 2012 · 2012
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Recognizing textual entailment: Models and applications
Ido Dagan, Dan Roth, Mark Sammons, and Fabio Massimo Zanzotto. 2013 · 2013
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Reporting bias and knowledge acquisition
Jonathan Gordon and Benjamin Van Durme. 2013 · 2013
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
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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 · 2016
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Path-based vs. distributional information in recognizing lexical semantic relations
Vered Shwartz and Ido Dagan. 2016 · 2016
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Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
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Commonsense for generative multi-hop question answering tasks
Lisa Bauer, Yicheng Wang, and Mohit Bansal. 2018 · 2018
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Ask the right questions: Active question reformulation with reinforcement learning
Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, and Wei Wang. 2018 · 2018
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
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Simple and effective semi-supervised question answering
Bhuwan Dhingra, Danish Danish, and Dheeraj Rajagopal. 2018 · 2018
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Reinforced dynamic reasoning for conversational question generation
Boyuan Pan, Hao Li, Ziyu Yao, Deng Cai, and Huan Sun. 2019 · 2019
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Ranking and selecting multi-hop knowledge paths to better predict human needs
Debjit Paul and Anette Frank. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Playing log (n)-questions over sentences
Peter Potash and Kaheer Suleman. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Explain Yourself! Leveraging Language Models for Commonsense Reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Soft layer-specific multi-task summarization with entailment and question generation
Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2018 · 2018
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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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The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
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Semeval-2018 task 11: Machine comprehension using commonsense knowledge
Simon Ostermann, Michael Roth, Ashutosh Modi, Stefan Thater, and Manfred Pinkal. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Self-training for jointly learning to ask and answer questions
Mrinmaya Sachan and Eric Xing. 2018 · 2018
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Paraphrase to explicate: Revealing implicit noun-compound relations
Vered Shwartz and Ido Dagan. 2018 · 2018
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Answer-based Adversarial Training for Generating Clarification Questions
Sudha Rao and Hal Daumé III. 2019 · 2019
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Social IQa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019b · 2019
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Learning to caption images through a lifetime by asking questions
Tingke Shen, Amlan Kar, and Sanja Fidler. 2019 · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Explicit utilization of general knowledge in machine reading comprehension
Chao Wang and Hui Jiang. 2019 · 2019
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Does it make sense? and why? a pilot study for sense making and explanation
Cunxiang Wang, Shuailong Liang, Yue Zhang, Xiaonan Li, and Tian Gao. 2019 · 2019
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Incorporating relation knowledge into commonsense reading comprehension with multi-task learning
Jiangnan Xia, Chen Wu, and Ming Yan. 2019 · 2019
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Improving question answering over incomplete KBs with knowledge-aware reader
Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, and William Yang Wang. 2019 · 2019
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Ben Zhou, Daniel Khashabi, Qiang Ning, and Dan Roth. 2019 · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
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Template-based question generation from retrieved sentences for improved unsupervised question answering
Alexander Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, and Bing Xiang. 2020 · 2020
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Tttttackling winogrande schemas
Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, and Jimmy Lin. 2020 · 2020
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Unsupervised question decomposition for question answering
Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela. 2020 · 2020
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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. 2020 · 2020
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WINOGRANDE: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
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Reasoning with heterogeneous knowledge for commonsense machine comprehension
Hongyu Lin, Le Sun, and Xianpei Han. 2017 · 2043
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Interpretation of natural language rules in conversational machine reading
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