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For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness.
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, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2019 · 1910
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Explaining collaborative filtering recommendations
Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000 · 2000
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001 · 2001
Earlier work this paper cites.
Thumbs up?: sentiment classification using machine learning techniques
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002 · 2002
Earlier work this paper cites.
The role of trust in automation reliance
Mary T Dzindolet, Scott A Peterson, Regina A Pomranky, Linda G Pierce, and Hall P Beck. 2003 · 2003
Earlier work this paper cites.
Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
Earlier work this paper cites.
Modeling annotators: A generative approach to learning from annotator rationales
Omar Zaidan and Jason Eisner. 2008 · 2008
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
Cited alongside, same era.
Fine-grained sentiment analysis with faithful attention
Ruiqi Zhong, Steven Shao, and Kathleen McKeown. 2019 · 2011
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
Cited alongside, same era.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015 · 2015
Cited alongside, same era.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Cited alongside, same era.
Fine-grained analysis of propaganda in news articles
Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeño, Rostislav Petrov, and Preslav Nakov. 2019 · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton. 2019 · 2019
Later among the works it cites.
Inferring which medical treatments work from reports of clinical trials
Eric Lehman, Jay DeYoung, Regina Barzilay, and Byron C. Wallace. 2019 · 2019
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The challenge of crafting intelligible intelligence
Daniel S Weld and Gagan Bansal. 2019 · 2019
Later among the works it cites.
Learning to faithfully rationalize by construction
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” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
The mythos of model interpretability
Zachary C Lipton. 2018 · 2018
Cited alongside, same era.
Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov. 2019 · 2019
Cited alongside, same era.
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace. 2020 · 2020
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An information bottleneck approach for controlling conciseness in rationale extraction
Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020 · 2020
Closest in time.