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The full power of human language-based communication cannot be realized without negation.
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, et al. 2020 · 1901
Earlier work this paper cites.
UNIFIEDQA: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
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.
Question answering is a format; when is it useful?
Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, and Sewon Min. 2019a · 1909
Earlier work this paper cites.
Question answering is a format; when is it useful?
Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, and Sewon Min. 2019b · 1909
Earlier work this paper cites.
Categories and De interpretatione
John L Ackrill et al. 1975 · 1975
Earlier work this paper cites.
A natural history of negation
Laurence Horn. 1989 · 1989
Earlier work this paper cites.
Using the framework
Robin Cooper, Dick Crouch, Jan Van Eijck, Chris Fox, Johan Van Genabith, Jan Jaspars, Hans Kamp, David Milward, Manfred Pinkal, and Massimo Poesio. 1996 · 1996
Earlier work this paper cites.
A semantic network of english verbs
Christiane Fellbaum. 1998 · 1998
Earlier work this paper cites.
The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
Earlier work this paper cites.
The bioscope corpus: biomedical texts annotated for uncertainty, negation and their scopes
Veronika Vincze, György Szarvas, Richárd Farkas, György Móra, and János Csirik. 2008 · 2008
Earlier work this paper cites.
Natural language processing with Python: analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
Earlier work this paper cites.
Sentiment classification considering negation and contrast transition
Shoushan Li and Chu-Ren Huang. 2009 · 2009
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Learning to summarize from human feedback
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F. Christiano. 2020 · 2009
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Semantic representation of negation using focus detection
Eduardo Blanco and Dan Moldovan. 2011 · 2011
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Types of common-sense knowledge needed for recognizing textual entailment
Peter LoBue and Alexander Yates. 2011 · 2011
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Annotation of negation cues and their scope: Guidelines v1
Roser Morante, Sarah Schrauwen, and Walter Daelemans. 2011 · 2011
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A review corpus annotated for negation, speculation and their scope
Natalia Konstantinova, Sheila C.M. de Sousa, Noa P. Cruz, Manuel J. Maña, Maite Taboada, and Ruslan Mitkov. 2012 · 2012
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*SEM 2012 shared task: Resolving the scope and focus of negation
Roser Morante and Eduardo Blanco. 2012 · 2012
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ConanDoyle-neg: Annotation of negation cues and their scope in conan doyle stories
Roser Morante and Walter Daelemans. 2012 · 2012
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Squibs: What is a paraphrase?
Rahul Bhagat and Eduard Hovy. 2013 · 2013
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NIL_UCM: Extracting drug-drug interactions from text through combination of sequence and tree kernels
Behrouz Bokharaeian and Alberto Díaz. 2013 · 2013
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Exploring negation annotations in the drugddi corpus
Behrouz Bokharaeian, Alberto Díaz, Mariana Neves, and Virginia Francisco. 2014 · 2014
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An empirical study on the effect of negation words on sentiment
Xiaodan Zhu, Hongyu Guo, Saif Mohammad, and Svetlana Kiritchenko. 2014 · 2014
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Translating negation: A manual error analysis
Federico Fancellu and Bonnie Webber. 2015 · 2015
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Negation scope detection for Twitter sentiment analysis
Johan Reitan, Jørgen Faret, Björn Gambäck, and Lars Bungum. 2015 · 2015
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DT-neg: Tutorial dialogues annotated for negation scope and focus in context
Rajendra Banjade and Vasile Rus. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Building a dictionary of affixal negations
Chantal van Son, Emiel van Miltenburg, and Roser Morante. 2016 · 2016
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Detecting negation scope is easy, except when it isn’t
Federico Fancellu, Adam Lopez, Bonnie Webber, and Hangfeng He. 2017 · 2017
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Neural networks for negation cue detection in Chinese
Hangfeng He, Federico Fancellu, and Bonnie Webber. 2017 · 2017
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RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
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Making neural QA as simple as possible but not simpler
Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017 · 2017
Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
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NegBERT: A transfer learning approach for negation detection and scope resolution
Aditya Khandelwal and Suraj Sawant. 2020 · 2020
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Easy, reproducible and quality-controlled data collection with CROWDAQ
Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasović, and Zhen Nie. 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, Peter J Liu, et al. 2020 · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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XNLI: Evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. 2018 · 2018
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How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C. Lipton. 2018 · 2018
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Learning with structured representations for negation scope extraction
Hao Li and Wei Lu. 2018 · 2018
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Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 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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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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What do models learn from question answering datasets?
Priyanka Sen and Amir Saffari. 2020 · 2020
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FLEX: unifying evaluation for few-shot NLP
Jonathan Bragg, Arman Cohan, Kyle Lo, and Iz Beltagy. 2021 · 2021
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A multilingual benchmark for probing negation-awareness with minimal pairs
Mareike Hartmann, Miryam de Lhoneux, Daniel Hershcovich, Yova Kementchedjhieva, Lukas Nielsen, Chen Qiu, and Anders Søgaard. 2021 · 2021
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Understanding by understanding not: Modeling negation in language models
Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, and Aaron Courville. 2021 · 2021
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“I’m not mad”: Commonsense implications of negation and contradiction
Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
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Multi-task learning of negation and speculation for targeted sentiment classification
Andrew Moore and Jeremy Barnes. 2021 · 2021
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What ingredients make for an effective crowdsourcing protocol for difficult NLU data collection tasks?
Nikita Nangia, Saku Sugawara, Harsh Trivedi, Alex Warstadt, Clara Vania, and Samuel R. Bowman. 2021 · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, S. Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Wei Yu, Vincent Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc Le, and Jason Wei. 2022 · 2022
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