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Work on "learning with rationales" shows that humans providing explanations to a machine learning system can improve the system's predictive accuracy.
Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
Earlier work this paper cites.
Data quality from crowdsourcing: A study of annotation selection criteria
Pei-Yun Hsueh, Prem Melville, and Vikas Sindhwani. 2009 · 2009
Earlier work this paper cites.
Increasing cheat robustness of crowdsourcing tasks
Carsten Eickhoff and Arjen P de Vries. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Reasoning about entailment with neural attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Earlier work this paper cites.
Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016 · 2016
Earlier work this paper cites.
Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
Cited alongside, same era.
Rationale-augmented convolutional neural networks for text classification
Ye Zhang, Iain Marshall, and Byron C Wallace. 2016 · 2016
Cited alongside, same era.
Human attention in visual question answering: Do humans and deep networks look at the same regions?
Abhishek Das, Harsh Agrawal, Larry Zitnick, Devi Parikh, and Dhruv Batra. 2017 · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Been Doshi-Velez, Finale; Kim. 2017 · 2017
Cited alongside, same era.
Explainable artificial intelligence (XAI)
David Gunning. 2017 · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Proceedings of the IJCAI Workshop on Explainable Artificial Intelligence
David Aha, editor. 2018 · 2018
Later among the works it cites.
Deriving machine attention from human rationales
Yujia Bao, Shiyu Chang, Mo Yu, and Regina Barzilay. 2018 · 2018
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. 2018 · 2018
Later among the works it cites.
Grounding visual explanations
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata. 2018 · 2018
Later among the works it cites.
Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen. 2018 · 2018
Later among the works it cites.
Multimodal explanations: Justifying decisions and pointing to the evidence
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Pang Wei Koh and Percy Liang. 2017 · 2017
Cited alongside, same era.
A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach. 2018 · 2018
Later among the works it cites.
Attention is not explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
Closest in time.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015 · 2057
Closest in time.