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To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for their predictions.
A survey on adversarial attacks and defenses in text
Wenqi Wang, Benxiao Tang, Run Wang, Lina Wang, and Aoshuang Ye. 2019 · 1902
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Behavior analysis of NLI models: Uncovering the influence of three factors on robustness
Vicente Iván Sánchez Carmona, Jeff Mitchell, and Sebastian Riedel. 2018 · 1985
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli. 2013 · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014 · 2014
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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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Reasoning about entailment with neural attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, and Phil Blunsom. 2015 · 2015
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell. 2016 · 2016
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Learning natural language inference using bidirectional LSTM model and inner-attention
Yang Liu, Chengjie Sun, Lei Lin, and Xiaolong Wang. 2016 · 2016
Cited alongside, same era.
Tsendsuren Munkhdalai and Hong Yu. 2016 · 2016
Cited alongside, same era.
“Why should I trust you?”: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2017 · 2017
Cited alongside, same era.
Grounding visual explanations (extended abstract)
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata. 2017 · 2017
e-SNLI: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Later among the works it cites.
Seq2Sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, and Cho-Jui Hsieh. 2018 · 2018
Later among the works it cites.
Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Textual explanations for self-driving vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John F. Canny, and Zeynep Akata. 2018 · 2018
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Adversarially regularising neural NLI models to integrate logical background knowledge
Pasquale Minervini and Sebastian Riedel. 2018 · 2018
Later among the works it cites.
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Cited alongside, same era.
Deceiving Google’s cloud video intelligence API built for summarizing videos
Hossein Hosseini, Baicen Xiao, and Radha Poovendran. 2017 · 2017
Cited alongside, same era.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee. 2017 · 2017
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
Later among the works it cites.
Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh. 2018 · 2018
Later among the works it cites.
Adversarial attacks on deep learning models in natural language processing: A survey
Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, and Chenliang Li. 2019 · 2019
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