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The common practice for training commonsense models has gone from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in order to train commonsense models.
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.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
Earlier work this paper cites.
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
Earlier work this paper cites.
Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
Earlier work this paper cites.
The measurement of observer agreement for categorical data
J Richard Landis and Gary G Koch. 1977 · 1977
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton. 2002 · 2002
Earlier work this paper cites.
Dare: Data augmented relation extraction with gpt-2
Yannis Papanikolaou and A. Pierleoni. 2020 · 2004
Earlier work this paper cites.
Genericskb: A knowledge base of generic statements
Sumithra Bhakthavatsalam, Chloe Anastasiades, and Peter E. Clark. 2020 · 2005
Earlier work this paper cites.
Freebase: a collaboratively created graph database for structuring human knowledge
Kurt D. Bollacker, Colin Evans, Praveen K. Paritosh, Tim Sturge, and Jamie Taylor. 2008 · 2008
Earlier work this paper cites.
Open information extraction: The second generation
Oren Etzioni, Anthony Fader, Janara Christensen, Stephen Soderland, et al. 2011 · 2011
Earlier work this paper cites.
N-gram counts and language models from the common crawl
Christian Buck, Kenneth Heafield, and Bas Van Ooyen. 2014 · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Dbpedia - a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, D. Kontokostas, Pablo N. Mendes, Sebastian Hellmann, M. Morsey, Patrick van Kleef, S. Auer, and C. Bizer. 2015 · 2015
Earlier work this paper cites.
Never-ending learning
Tom Michael Mitchell, William W. Cohen, Estevam R. Hruschka, Partha P. Talukdar, Bo Yang, Justin Betteridge, Andrew Carlson, Bhavana Dalvi, Matt Gardner, Bryan Kisiel, Jayant Krishnamurthy, N. Lao, Kathryn Mazaitis, Thahir Mohamed, Ndapandula Nakashole, Emmanouil Antonios Platanios, Alan Ritter, Mehdi Samadi, Burr Settles, Richard C. Wang, D. Wijaya, Abhinav Gupta, Xinlei Chen, Abulhair Saparov, Malcolm Greaves, and Joel Welling. 2015 · 2015
Earlier work this paper cites.
Sequence-level knowledge distillation
Yoon Kim and Alexander M. Rush. 2016 · 2016
Earlier work this paper cites.
Causal generative models are just a start
Ernest Davis and Gary Marcus. 2017 · 2017
Earlier work this paper cites.
The effect of different writing tasks on linguistic style: A case study of the ROC story cloze task
Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A. Smith. 2017 · 2017
Cited alongside, same era.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
Cited alongside, same era.
Don’t just assume; look and answer: Overcoming priors for visual question answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi. 2018 · 2018
Cited alongside, same era.
Performance impact caused by hidden bias of training data for recognizing textual entailment
Masatoshi Tsuchiya. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Comet-atomic 2020: On symbolic and neural commonsense knowledge graphs
Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. 2021 · 2020
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A wizard-of-oz interface and persona-based methodology for collecting health counseling dialog
William R. Kearns, Neha Kaura, Myra Divina, Cuong Viet Vo, Dong Si, Teresa M. Ward, and Weichao Yuwen. 2020 · 2020
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Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2020
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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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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2020 · 2020
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Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. 2018 · 2018
Cited alongside, same era.
Comet: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, A. Çelikyilmaz, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander M Rush. 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, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Atomic: An atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Do not have enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2020 · 2020
Cited alongside, same era.
Wenhan Xiong, Jingfei Du, William Yang Wang, and Veselin Stoyanov. 2020 · 2020
Later among the works it cites.
Generative data augmentation for commonsense reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, and Doug Downey. 2020 · 2020
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Aser: A large-scale eventuality knowledge graph
Hongming Zhang, Xin Liu, Haojie Pan, Y. Song, and C. Leung. 2020b · 2020
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Temporal common sense acquisition with minimal supervision
Ben Zhou, Qiang Ning, Daniel Khashabi, and Dan Roth. 2020 · 2020
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Conversational multi-hop reasoning with neural commonsense knowledge and symbolic logic rules
Forough Arabshahi, Jennifer Lee, Antoine Bosselut, Yejin Choi, and Tom. Mitchell. 2021 · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
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MERMAID: Metaphor generation with symbolism and discriminative decoding
Tuhin Chakrabarty, Xurui Zhang, Smaranda Muresan, and Nanyun Peng. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Provable limitations of acquiring meaning from ungrounded form: What will future language models understand?
William Merrill, Yoav Goldberg, Roy Schwartz, and Noah A. Smith. 2021 · 2021
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Commonsenseqa 2.0: Exposing the limits of ai through gamification
Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, and Jonathan Berant. 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
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