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With deep neural models increasingly permeating our daily lives comes a need for transparent and comprehensible explanations of their decision-making.
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Lloyd S Shapley · 1997
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko · 2007
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Modeling semantic containment and exclusion in natural language inference
Bill MacCartney and Christopher D. Manning · 2008
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Neural machine translation in linear time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu · 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
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Supervised learning of universal sentence representations from natural language inference data
A Conneau, D Kiela, H Schwenk, L Barrault, and A Bordes · 2017
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin · 2017
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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The promise and peril of human evaluation for model interpretability
Bernease Herman · 2017
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What do we need to build explainable ai systems for the medical domain?
Andreas Holzinger, Chris Biemann, Constantinos S Pattichis, and Douglas B Kell · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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e-snli: Natural language inference with natural language explanations
Rethinking cooperative rationalization: Introspective extraction and complement control
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi S Jaakkola · 2019
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From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom · 2018
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Rationalization: A neural machine translation approach to generating natural language explanations
Upol Ehsan, Brent Harrison, Larry Chan, and Mark O Riedl · 2018
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Evaluating feature importance estimates
Sara Hooker, Dumitru Erhan, Pieter jan Kindermans, and Been Kim · 2018
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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
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Faithful multimodal explanation for visual question answering
Jialin Wu and Raymond J Mooney · 2018
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Can i trust the explainer? verifying post- hoc explanatory methods
Phil Blunsom, Oana-Maria Camburu, Jakob Foerster, Eleonora Giunchiglia, and Thomas Lukasiewicz · 2019
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Explaining deep neural networks
Oana-Maria Camburu · 2020
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Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu · 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
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
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Captum: A unified and generic model interpretability library for pytorch, 2020
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson · 2020
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Natural language rationales with full-stack visual reasoning: From pixels to semantic frames to commonsense graphs
Ana Marasović, Chandra Bhagavatula, Jae sung Park, Ronan Le Bras, Noah A Smith, and Yejin Choi · 2020
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Visualizing the impact of feature attribution baselines
Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2020
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The many shapley values for model explanation
Mukund Sundararajan and Amir Najmi · 2020
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2020
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e-vil: A dataset and benchmark for natural language explanations in vision-language tasks
Maxime Kayser, Oana-Maria Camburu, Leonard Salewski, Cornelius Emde, Virginie Do, Zeynep Akata, and Thomas Lukasiewicz · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasović, and Noah A Smith · 2021
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Axiomatic foundations of explainability
Leila Amgoud and Jonathan Ben-Naim · 2022
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Diagnosing ai explanation methods with folk concepts of behavior
Alon Jacovi, Jasmijn Bastings, Sebastian Gehrmann, Yoav Goldberg, and Katja Filippova · 2022
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Nlx-gpt: A model for natural language explanations in vision and vision-language tasks
Fawaz Sammani, Tanmoy Mukherjee, and Nikos Deligiannis · 2022
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