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Neural predictive models have achieved remarkable performance improvements in various natural language processing tasks.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
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A coefficient of agreement for nominal scales
Jacob Cohen · 1960
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Self-explanations: How students study and use examples in learning to solve problems
Michelene TH Chi, Miriam Bassok, Matthew W Lewis, Peter Reimann, and Robert Glaser · 1989
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A model of the self-explanation effect
Kurt VanLehn, Randolph M Jones, and Michelene TH Chi · 1992
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Eliciting self-explanations improves understanding
Michelene TH Chi, Nicholas De Leeuw, Mei-Hung Chiu, and Christian LaVancher · 1994
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Aligning faithful interpretations with their social attribution
Alon Jacovi and Yoav Goldberg · 2006
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Evaluating explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C Lipton, Graham Neubig, and William W Cohen · 2012
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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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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
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"why should I trust you?": Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 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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Enhanced LSTM for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen · 2017
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom · 2018
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S. Jaakkola · 2018
Cited alongside, same era.
The mythos of model interpretability
Zachary C. Lipton · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S. Jaakkola · 2018
Cited alongside, same era.
Natural language inference over interaction space
Yichen Gong, Heng Luo, and Jian Zhang · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2020
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A survey on explainable artificial intelligence (xai): Toward medical xai
Erico Tjoa and Cuntai Guan · 2020
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Explaining Question Answering Models through Text Generation
Veronica Latcinnik and Jonathan Berant · 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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NILE : Natural language inference with faithful natural language explanations
Sawan Kumar and Partha Talukdar · 2020
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Cited alongside, same era.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
Cited alongside, same era.
Generating token-level explanations for natural language inference
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal · 2019
Cited alongside, same era.
Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov · 2019
Cited alongside, same era.
Attention is not Explanation
Sarthak Jain and Byron C. Wallace · 2019
Cited alongside, same era.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
Cited alongside, same era.
Sarah Wiegreffe, Ana Marasovic, and Noah A. Smith · 2020
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Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace · 2020
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Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language?
Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal · 2020
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Towards interpretable natural language understanding with explanations as latent variables
Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, and Jian Tang · 2020
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WT5?! Training Text-to-Text Models to Explain their Predictions
Sharan Narang, Colin Raffel, Katherine J. Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan · 2020
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To what extent do human explanations of model behavior align with actual model behavior?
Grusha Prasad, Yixin Nie, Mohit Bansal, Robin Jia, Douwe Kiela, and Adina Williams · 2020
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Evaluating and characterizing human rationales
Samuel Carton, Anirudh Rathore, and Chenhao Tan · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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mt5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel · 2020
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
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Lirex: Augmenting language inference with relevant explanation
Xinyan Zhao and V. G. Vinod Vydiswaran · 2021
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