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Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019b · 1906
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Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, and Sébastien Martin. 2019 · 1907
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One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yi Zhang. 2019 · 1909
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The distance-weighted k-nearest-neighbor rule
Sahibsingh A Dudani. 1976 · 1976
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Building applied natural language generation systems
Ehud Reiter and Robert Dale. 1997 · 1997
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Global Sensitivity Analysis: The Primer
A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola. 2008 · 2008
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
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Explaining the stars: Weighted multiple-instance learning for aspect-based sentiment analysis
Nikolaos Pappas and Andrei Popescu-Belis. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Visualizing and understanding neural models in nlp
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2015 · 2015
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Learning to explain entity relationships in knowledge graphs
Nikos Voskarides, Edgar Meij, Manos Tsagkias, Maarten de Rijke, and Wouter Weerkamp. 2015 · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, , and Victor Lempitsky. 2016 · 2016
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Actual Causality
Joseph Y. Halpern. 2016 · 2016
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A meaning-based English math word problem solver with understanding, reasoning and explanation
Chao-Chun Liang, Shih-Hong Tsai, Ting-Yun Chang, Yi-Chung Lin, and Keh-Yih Su. 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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Quint: Interpretable question answering over knowledge bases
Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum. 2017 · 2017
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Using regional saliency for speech emotion recognition
Zakaria Aldeneh and Emily Mower Provost. 2017 · 2017
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi Jaakkola. 2017 · 2017
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Detecting and explaining causes from text for a time series event
Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen, and Eduard Hovy. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Unsupervised, knowledge-free, and interpretable word sense disambiguation
Alexander Panchenko, Fide Marten, Eugen Ruppert, Stefano Faralli, Dmitry Ustalov, Simone Paolo Ponzetto, and Chris Biemann. 2017 · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez. 2017 · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 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 · 2017
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An interpretable knowledge transfer model for knowledge base completion
Qizhe Xie, Xuezhe Ma, Zihang Dai, and Eduard Hovy. 2017 · 2017
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Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
A. Adadi and M. Berrada. 2018 · 2018
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Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph
AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. 2018 · 2018
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Interpretable emoji prediction via label-wise attention LSTMs
Francesco Barbieri, Luis Espinosa-Anke, Jose Camacho-Collados, Steven Schockaert, and Horacio Saggion. 2018 · 2018
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Exploiting structure in representation of named entities using active learning
Nikita Bhutani, Kun Qian, Yunyao Li, H. V. Jagadish, Mauricio Hernandez, and Mitesh Vasa. 2018 · 2018
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Extractive adversarial networks: High-recall explanations for identifying personal attacks in social media posts
Samuel Carton, Qiaozhu Mei, and Paul Resnick. 2018 · 2018
Cited alongside, same era.
MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
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Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo V. Carvalho, Eduardo M. Pereira, and Jaime S. Cardoso. 2019 · 2019
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Auditing deep learning processes through kernel-based explanatory models
Danilo Croce, Daniele Rossini, and Roberto Basili. 2019 · 2019
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EditNTS: An neural programmer-interpreter model for sentence simplification through explicit editing
Yue Dong, Zichao Li, Mehdi Rezagholizadeh, and Jackie Chi Kit Cheung. 2019 · 2019
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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Self-assembling modular networks for interpretable multi-hop reasoning
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Explaining non-linear classifier decisions within kernel-based deep architectures
Danilo Croce, Daniele Rossini, and Roberto Basili. 2018 · 2018
Cited alongside, same era.
Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018 · 2018
Cited alongside, same era.
Predicting and interpreting embeddings for out of vocabulary words in downstream tasks
Nicolas Garneau, Jean-Samuel Leboeuf, and Luc Lamontagne. 2018 · 2018
Cited alongside, same era.
Interpreting recurrent and attention-based neural models: a case study on natural language inference
Reza Ghaeini, Xiaoli Fern, and Prasad Tadepalli. 2018 · 2018
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Explaining character-aware neural networks for word-level prediction: Do they discover linguistic rules?
Fréderic Godin, Kris Demuynck, Joni Dambre, Wesley De Neve, and Thomas Demeester. 2018 · 2018
Cited alongside, same era.
A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018 · 2018
Cited alongside, same era.
LISA: Explaining recurrent neural network judgments via layer-wIse semantic accumulation and example to pattern transformation
Pankaj Gupta and Hinrich Schütze. 2018 · 2018
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Yichen Jiang and Mohit Bansal. 2019 · 2019
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Explore, propose, and assemble: An interpretable model for multi-hop reading comprehension
Yichen Jiang, Nitish Joshi, Yen-Chun Chen, and Mohit Bansal. 2019 · 2019
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Human-grounded evaluations of explanation methods for text classification
Piyawat Lertvittayakumjorn and Francesca Toni. 2019 · 2019
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CNM: An interpretable complex-valued network for matching
Qiuchi Li, Benyou Wang, and Massimo Melucci. 2019 · 2019
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Towards explainable NLP: A generative explanation framework for text classification
Hui Liu, Qingyu Yin, and William Yang Wang. 2019 · 2019
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Constructing interpretive spatio-temporal features for multi-turn responses selection
Junyu Lu, Chenbin Zhang, Zeying Xie, Guang Ling, Tom Chao Zhou, and Zenglin Xu. 2019 · 2019
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OpenDialKG: Explainable conversational reasoning with attention-based walks over knowledge graphs
Seungwhan Moon, Pararth Shah, Anuj Kumar, and Rajen Subba. 2019 · 2019
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Investigating robustness and interpretability of link prediction via adversarial modifications
Pouya Pezeshkpour, Yifan Tian, and Sameer Singh. 2019a · 2019
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Investigating robustness and interpretability of link prediction via adversarial modifications
Pouya Pezeshkpour, Yifan Tian, and Sameer Singh. 2019b · 2019
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Learning interpretable negation rules via weak supervision at document level: A reinforcement learning approach
Nicolas Pröllochs, Stefan Feuerriegel, and Dirk Neumann. 2019 · 2019
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Train, sort, explain: Learning to diagnose translation models
Robert Schwarzenberg, David Harbecke, Vivien Macketanz, Eleftherios Avramidis, and Sebastian Möller. 2019 · 2019
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HEIDL: Learning linguistic expressions with deep learning and human-in-the-loop
Prithviraj Sen, Yunyao Li, Eser Kandogan, Yiwei Yang, and Walter Lasecki. 2019 · 2019
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Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
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Interpretable question answering on knowledge bases and text
Alona Sydorova, Nina Poerner, and Benjamin Roth. 2019 · 2019
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Generating token-level explanations for natural language inference
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2019 · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
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Interpretable relevant emotion ranking with event-driven attention
Yang Yang, Deyu Zhou, Yulan He, and Meng Zhang. 2019 · 2019
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An interpretable reasoning network for multi-relation question answering
Mantong Zhou, Minlie Huang, and Xiaoyan Zhu. 2018 · 2019
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Explainable ai: Foundations, industrial applications, practical challenges, and lessons learned
Freddy Lecue, Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly, Riccardo Guidotti, and Pasquale Minervini. 2020 · 2020
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Interpreting neural networks to improve politeness comprehension
M. Aubakirova and M. Bansal. 2016 · 2041
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