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A growing effort in NLP aims to build datasets of human explanations.
A richly annotated corpus for different tasks in automated fact-checking
Andreas Hanselowski, Christian Stab, Claudia Schulz, Zile Li, and Iryna Gurevych. 2019 · 1911
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Studies in the logic of explanation
Carl G Hempel and Paul Oppenheim. 1948 · 1948
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Telling more than we can know: verbal reports on mental processes
Richard E Nisbett and Timothy D Wilson. 1977 · 1977
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Measuring individual differences in implicit cognition: the implicit association test
Anthony G Greenwald, Debbie E McGhee, and Jordan LK Schwartz. 1998 · 1998
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The shadows and shallows of explanation
Robert A Wilson and Frank Keil. 1998 · 1998
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Fact or fiction: verifying scientific claims
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020 · 2004
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Explanation and understanding
Frank C Keil. 2006 · 2006
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The structure and function of explanations
Tania Lombrozo. 2006 · 2006
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Mixed motives and racial bias: The impact of legitimate and illegitimate criteria on decision making
Michael I Norton, Samuel R Sommers, Joseph A Vandello, and John M Darley. 2006 · 2006
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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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Qed: A framework and dataset for explanations in question answering
Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, and Michael Collins. 2020 · 2009
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Learning to rationalize for nonmonotonic reasoning with distant supervision
Faeze Brahman, Vered Shwartz, Rachel Rudinger, and Yejin Choi. 2020 · 2012
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Edited media understanding: Reasoning about implications of manipulated images
Jeff Da, Maxwell Forbes, Rowan Zellers, Anthony Zheng, Jena D Hwang, Antoine Bosselut, and Yejin Choi. 2020 · 2012
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You had me at hello: How phrasing affects memorability
Cristian Danescu-Niculescu-Mizil, Justin Cheng, Jon Kleinberg, and Lillian Lee. 2012 · 2012
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Learning to parse natural language commands to a robot control system
Cynthia Matuszek, Evan Herbst, Luke Zettlemoyer, and Dieter Fox. 2013 · 2013
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Learning from natural instructions
Dan Goldwasser and Dan Roth. 2014 · 2014
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The effect of wording on message propagation: Topic- and author-controlled natural experiments on twitter
Chenhao Tan, Lillian Lee, and Bo Pang. 2014 · 2014
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What’s in an explanation? characterizing knowledge and inference requirements for elementary science exams
Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, and Peter Clark. 2016 · 2016
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Joint concept learning and semantic parsing from natural language explanations
Shashank Srivastava, Igor Labutov, and Tom Mitchell. 2017 · 2017
Cited alongside, same era.
Where is your evidence: Improving fact-checking by justification modeling
Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018 · 2018
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.
Extractive adversarial networks: High-recall explanations for identifying personal attacks in social media posts
Samuel Carton, Qiaozhu Mei, and Paul Resnick. 2018 · 2018
Cited alongside, same era.
Training classifiers with natural language explanations
Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, and Christopher Ré. 2018 · 2018
Cited alongside, same era.
Eraser: A benchmark to evaluate rationalized nlp models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace. 2020 · 2020
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e-snli-ve-2.0: Corrected visual-textual entailment with natural language explanations
Virginie Do, Oana-Maria Camburu, Zeynep Akata, and Thomas Lukasiewicz. 2020 · 2020
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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
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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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WorldTree: A corpus of explanation graphs for elementary science questions supporting multi-hop inference
Peter Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton Morrison. 2018 · 2018
Cited alongside, same era.
Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
Cited alongside, same era.
Textual explanations for self-driving vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, and Zeynep Akata. 2018 · 2018
Cited alongside, same era.
VQA-E: Explaining, elaborating, and enhancing your answers for visual questions
Qing Li, Qingyi Tao, Shafiq Joty, Jianfei Cai, and Jiebo Luo. 2018 · 2018
Cited alongside, same era.
Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach. 2018 · 2018
Cited alongside, same era.
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.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
Cited alongside, same era.
Explainable automated fact-checking for public health claims
Neema Kotonya and Francesca Toni. 2020 · 2020
Later among the works it cites.
Annotator rationales for labeling tasks in crowdsourcing
Mucahid Kutlu, Tyler McDonnell, Matthew Lease, and Tamer Elsayed. 2020 · 2020
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“why is ‘chicago’ deceptive?” towards building model-driven tutorials for humans
Vivian Lai, Han Liu, and Chenhao Tan. 2020 · 2020
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What is more likely to happen next? video-and-language future event prediction
Jie Lei, Licheng Yu, Tamara Berg, and Mohit Bansal. 2020 · 2020
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ESPRIT: Explaining solutions to physical reasoning tasks
Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, and Dragomir Radev. 2020 · 2020
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Social bias frames: Reasoning about social and power implications of language
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A Smith, and Yejin Choi. 2020 · 2020
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Human attention maps for text classification: Do humans and neural networks focus on the same words?
Cansu Sen, Thomas Hartvigsen, Biao Yin, Xiangnan Kong, and Elke Rundensteiner. 2020 · 2020
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Learning from explanations with neural execution tree
Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, and Xiang Ren. 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.
Psychological foundations of explainability and interpretability in artificial intelligence
David A Broniatowski et al. 2021 · 2021
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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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Teach me to explain: A review of datasets for explainable nlp
Sarah Wiegreffe and Ana Marasović. 2021 · 2021
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