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A key challenge facing natural language interfaces is enabling users to understand the capabilities of the underlying system.
Universal adversarial triggers for attacking and analyzing nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 1908
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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A statistical semantic parser that integrates syntax and semantics
Ruifang Ge and Raymond Mooney. 2005 · 2005
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Beyond accuracy: Behavioral testing of nlp models with checklist
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2005
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Weakly supervised learning of semantic parsers for mapping instructions to actions
Yoav Artzi and Luke Zettlemoyer. 2013 · 2013
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang. 2015 · 2015
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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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Learning language games through interaction
Sida I Wang, Percy Liang, and Christopher D Manning. 2016 · 2016
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017 · 2017
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Naturalizing a programming language via interactive learning
Sida I Wang, Samuel Ginn, Percy Liang, and Christoper D Manning. 2017 · 2017
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S Jaakkola. 2018 · 2018
Generating counterfactual explanations with natural language
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata. 2018 · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni. 2018 · 2018
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Towards explainable nlp: A generative explanation framework for text classification
Hui Liu, Qingyu Yin, and William Yang Wang. 2018 · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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Tranx: A transition-based neural abstract syntax parser for semantic parsing and code generation
Pengcheng Yin and Graham Neubig. 2018 · 2018
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Cited alongside, same era.
Babyai: A platform to study the sample efficiency of grounded language learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, and Yoshua Bengio. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Neural semantic parsing
Matt Gardner, Pradeep Dasigi, Srinivasan Iyer, Alane Suhr, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Learning models for actionable recourse
Alexis Ross, Himabindu Lakkaraju, and Osbert Bastani. 2021a
Cited in the paper.
Explaining nlp models via minimal contrastive editing (mice)
Alexis Ross, Ana Marasović, and Matthew E Peters. 2021b
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu. 2019 · 2019
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Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel S Weld. 2021 · 2021
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