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Rationalization is fundamental to human reasoning and learning.
Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
Earlier work this paper cites.
Lirex: Augmenting language inference with relevant explanation
Xinyan Zhao and V. G. Vinod Vydiswaran. 2021 · 2012
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Explanatory preferences shape learning and inference
Tania Lombrozo. 2016 · 2016
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S. Jaakkola. 2018 · 2018
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 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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Training classifiers with natural language explanations
Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, and Christopher Ré. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Explanation in Artificial Intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 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
Earlier work this paper cites.
Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 2019
Earlier work this paper cites.
Evaluating and characterizing human rationales
Samuel Carton, Anirudh Rathore, and Chenhao Tan. 2020 · 2020
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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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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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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 · 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
Explaining NLP models via minimal contrastive editing (MiCE)
Alexis Ross, Ana Marasović, and Matthew Peters. 2021 · 2021
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Does external knowledge help explainable natural language inference? automatic evaluation vs. human ratings
Hendrik Schuff, Hsiu-Yu Yang, Heike Adel, and Ngoc Thang Vu. 2021 · 2021
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Get your vitamin C! robust fact verification with contrastive evidence
Tal Schuster, Adam Fisch, and Regina Barzilay. 2021 · 2021
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Avoiding inference heuristics in few-shot prompt-based finetuning
Prasetya Utama, Nafise Sadat Moosavi, Victor Sanh, and Iryna Gurevych. 2021 · 2021
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Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasović, and Noah A. Smith. 2021 · 2021
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Explanations for CommonsenseQA: New Dataset and Models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021 · 2021
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Self-training with few-shot rationalization
Meghana Moorthy Bhat, Alessandro Sordoni, and Subhabrata Mukherjee. 2021 · 2021
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Competency problems: On finding and removing artifacts in language data
Matt Gardner, William Merrill, Jesse Dodge, Matthew Peters, Alexis Ross, Sameer Singh, and Noah A. Smith. 2021 · 2021
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Peter Hase and Mohit Bansal. 2021 · 2021
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Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg. 2021a · 2021
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Contrastive explanations for model interpretability
Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, and Yoav Goldberg. 2021b · 2021
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Explaining the efficacy of counterfactually augmented data
Divyansh Kaushik, Amrith Setlur, Eduard Hovy, and Zachary C Lipton. 2021 · 2021
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Howard Chen, Jacqueline He, Karthik Narasimhan, and Danqi Chen. 2022 · 2022
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Can language models learn from explanations in context?
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, and Felix Hill. 2022 · 2022
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On advances in text generation from images beyond captioning: A case study in self-rationalization
Shruti Palaskar, Akshita Bhagia, Yonatan Bisk, Florian Metze, Alan W. Black, and Ana Marasović. 2022 · 2022
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Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?
Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, and William W. Cohen. 2022 · 2022
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Supervising model attention with human explanations for robust natural language inference
Joe Stacey, Yonatan Belinkov, and Marek Rei. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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Generating data to mitigate spurious correlations in natural language inference datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp, and Pradeep Dasigi. 2022 · 2022
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The unreliability of explanations in few-shot in-context learning
Xi Ye and Greg Durrett. 2022 · 2022
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Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, and Noah D. Goodman. 2022 · 2022
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