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We present ProtoTEx, a novel white-box NLP classification architecture based on prototype networks.
An introduction to case-based reasoning
Janet L Kolodner. 1992 · 1992
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Prototype selection for interpretable classification
Jacob Bien and Robert Tibshirani. 2011 · 2011
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An empirical investigation of statistical significance in NLP
Taylor Berg-Kirkpatrick, David Burkett, and Dan Klein. 2012 · 2012
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*SEM 2013 shared task: Semantic textual similarity
Eneko Agirre, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, and Weiwei Guo. 2013 · 2013
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Eye Tracking the User Experience: A Practical Guide to Research
Agnieszka Bojko. 2013 · 2013
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The bayesian case model: A generative approach for case-based reasoning and prototype classification
Been Kim, Cynthia Rudin, and Julie A Shah. 2014 · 2014
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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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
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016 · 2016
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Pervasive attention: 2D convolutional neural networks for sequence-to-sequence prediction
Maha Elbayad, Laurent Besacier, and Jakob Verbeek. 2018 · 2018
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Generating sentences by editing prototypes
Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, and Percy Liang. 2018 · 2018
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Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin. 2018 · 2018
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Believe it or not: Designing a human-ai partnership for mixed-initiative fact-checking
An T Nguyen, Aditya Kharosekar, Saumyaa Krishnan, Siddhesh Krishnan, Elizabeth Tate, Byron C Wallace, and Matthew Lease. 2018 · 2018
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DeClarE: Debunking fake news and false claims using evidence-aware deep learning
Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2018 · 2018
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Guidelines for human-ai interaction
Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al. 2019 · 2019
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Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov. 2019 · 2019
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This looks like that: Deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su. 2019 · 2019
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Fine-grained analysis of propaganda in news article
Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeño, Rostislav Petrov, and Preslav Nakov. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Exfakt: A framework for explaining facts over knowledge graphs and text
Mohamed H. Gad-Elrab, Daria Stepanova, Jacopo Urbani, and Gerhard Weikum. 2019 · 2019
Cited alongside, same era.
Why do you think that? exploring faithful sentence-level rationales without supervision
Max Glockner, Ivan Habernal, and Iryna Gurevych. 2020 · 2020
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Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal. 2020 · 2020
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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2020
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Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace. 2020 · 2020
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Explainable automated fact-checking for public health claims
Neema Kotonya and Francesca Toni. 2020b · 2020
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Annotator Rationales for Labeling Tasks in Crowdsourcing
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Annotating and analyzing the interactions between meaning relations
Darina Gold, Venelin Kovatchev, and Torsten Zesch. 2019 · 2019
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Interpretable image recognition with hierarchical prototypes
Peter Hase, Chaofan Chen, Oscar Li, and Cynthia Rudin. 2019 · 2019
Cited alongside, same era.
Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Cited alongside, same era.
Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
defend: Explainable fake news detection
Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019 · 2019
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Designing theory-driven user-centric explainable AI
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. 2019 · 2019
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Mucahid Kutlu, Tyler McDonnell, Tamer Elsayed, and Matthew Lease. 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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Questioning the AI: informing design practices for explainable ai user experiences
Q Vera Liao, Daniel Gruen, and Sarah Miller. 2020 · 2020
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Learning to deceive with attention-based explanations
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, and Zachary C. Lipton. 2020 · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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Trustworthy AI development guidelines for human system interaction
Chathurika S Wickramasinghe, Daniel L Marino, Javier Grandio, and Milos Manic. 2020 · 2020
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Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021 · 2021
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A survey on computational propaganda detection
Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, and Preslav Nakov. 2021 · 2021
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Automated fact-checking for assisting human fact-checkers
Preslav Nakov, D. Corney, Maram Hasanain, Firoj Alam, Tamer Elsayed, A. Barr’on-Cedeno, Paolo Papotti, Shaden Shaar, and Giovanni Da San Martino. 2021 · 2021
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SELFEXPLAIN: A self-explaining architecture for neural text classifiers
Dheeraj Rajagopal, Vidhisha Balachandran, Eduard H Hovy, and Yulia Tsvetkov. 2021 · 2021
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Putting humans in the natural language processing loop: A survey
Zijie J. Wang, Dongjin Choi, Shenyu Xu, and Diyi Yang. 2021 · 2021
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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
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