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Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context.
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.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L Hamilton, David Duvenaud, Raquel Urtasun, and Richard S Zemel. 2019 · 1910
Earlier work this paper cites.
Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 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, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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A large-scale dataset for argument quality ranking: Construction and analysis
Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov, and Noam Slonim. 2019 · 1911
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Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms
Tom Leighton and Satish Rao. 1999 · 1999
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang. 2020 · 2001
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Conceptnet—a practical commonsense reasoning tool-kit
Hugo Liu and Push Singh. 2004 · 2004
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Wt5?! training text-to-text models to explain their predictions
Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. 2020 · 2004
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
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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. 2020 · 2010
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Eigen: Event influence generation using pre-trained language models
Aman Madaan, Dheeraj Rajagopal, Yiming Yang, Abhilasha Ravichander, Eduard Hovy, and Shrimai Prabhumoye. 2020 · 2010
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Braid: Weaving symbolic and neural knowledge into coherent logical explanations
Aditya Kalyanpur, Tom Breloff, David Ferrucci, Adam Lally, and John Jantos. 2020 · 2011
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Why are you taking this stance? identifying and classifying reasons in ideological debates
Kazi Saidul Hasan and Vincent Ng. 2014 · 2014
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An exact graph edit distance algorithm for solving pattern recognition problems
Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, and Patrick Martineau. 2015 · 2015
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Commonsense reasoning and commonsense knowledge in artificial intelligence
Ernest Davis and Gary Marcus. 2015 · 2015
Earlier work this paper cites.
Image retrieval using scene graphs
Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, and Li Fei-Fei. 2015 · 2015
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Which argument is more convincing? analyzing and predicting convincingness of web arguments using bidirectional lstm
Ivan Habernal and Iryna Gurevych. 2016 · 2016
Earlier work this paper cites.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Earlier work this paper cites.
Commonsense knowledge base completion
Xiang Li, Aynaz Taheri, Lifu Tu, and Kevin Gimpel. 2016 · 2016
Earlier work this paper cites.
Semeval-2016 task 6: Detecting stance in tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016 · 2016
Earlier work this paper cites.
Semeval-2017 task 8: Rumoureval: Determining rumour veracity and support for rumours
Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga. 2017 · 2017
Earlier work this paper cites.
Scene graph generation by iterative message passing
Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei. 2017 · 2017
Cited alongside, same era.
e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith. 2018 · 2018
Cited alongside, same era.
Peter A Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton T Morrison. 2018 · 2018
Cited alongside, same era.
Mining cross-cultural differences and similarities in social media
Bill Yuchen Lin, Frank F Xu, Kenny Zhu, and Seung-won Hwang. 2018 · 2018
Cited alongside, same era.
Social chemistry 101: Learning to reason about social and moral norms
Maxwell Forbes, Jena D Hwang, Vered Shwartz, Maarten Sap, and Yejin Choi. 2020 · 2020
Later among the works it cites.
R4c: A benchmark for evaluating rc systems to get the right answer for the right reason
Naoya Inoue, Pontus Stenetorp, and Kentaro Inui. 2020 · 2020
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Learning to explain: Datasets and models for identifying valid reasoning chains in multihop question-answering
Harsh Jhamtani and Peter Clark. 2020 · 2020
Later among the works it cites.
Qasc: A dataset for question answering via sentence composition
Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020 · 2020
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Commongen: A constrained text generation challenge for generative commonsense reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020 · 2020
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis. 2018 · 2018
Cited alongside, same era.
Automatic extraction of commonsense locatednear knowledge
Frank F Xu, Bill Yuchen Lin, and Kenny Zhu. 2018 · 2018
Cited alongside, same era.
Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
Cited alongside, same era.
Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Are you convinced? choosing the more convincing evidence with a siamese network
Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, and Noam Slonim. 2019 · 2019
Cited alongside, same era.
Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon. 2019 · 2019
Cited alongside, same era.
Natural language rationales with full-stack visual reasoning: From pixels to semantic frames to commonsense graphs
Ana Marasović, Chandra Bhagavatula, Jae sung Park, Ronan Le Bras, Noah A Smith, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Glucose: Generalized and contextualized story explanations
Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, and Jennifer Chu-Carroll. 2020 · 2020
Later among the works it cites.
Evaluating explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, and William W. Cohen. 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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PRover: Proof generation for interpretable reasoning over rules
Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, and Mohit Bansal. 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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Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P Parikh. 2020 · 2020
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Worldtree v2: A corpus of science-domain structured explanations and inference patterns supporting multi-hop inference
Zhengnan Xie, Sebastian Thiem, Jaycie Martin, Elizabeth Wainwright, Steven Marmorstein, and Peter Jansen. 2020 · 2020
Later among the works it cites.
Teaching machine comprehension with compositional explanations
Qinyuan Ye, Xiao Huang, Elizabeth Boschee, and Xiang Ren. 2020 · 2020
Later among the works it cites.
Winowhy: A deep diagnosis of essential commonsense knowledge for answering winograd schema challenge
Hongming Zhang, Xinran Zhao, and Yangqiu Song. 2020 · 2020
Later among the works it cites.
Bertscore: Evaluating text generation with bert
Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Later among the works it cites.
Learning to rationalize for nonmonotonic reasoning with distant supervision
Faeze Brahman, Vered Shwartz, Rachel Rudinger, and Yejin Choi. 2021 · 2021
Closest in time.
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021 · 2021
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A survey on stance detection for mis-and disinformation identification
Momchil Hardalov, Arnav Arora, Preslav Nakov, and Isabelle Augenstein. 2021 · 2021
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Unicorn on rainbow: A universal commonsense reasoning model on a new multitask benchmark
Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
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multiPRover: Generating multiple proofs for improved interpretability in rule reasoning
Swarnadeep Saha, Prateek Yadav, and Mohit Bansal. 2021 · 2021
Closest in time.
Teach me to explain: A review of datasets for explainable nlp
Sarah Wiegreffe and Ana Marasović. 2021 · 2021
Closest in time.