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A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility and trust.
A bert baseline for the natural questions
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Boolq: Exploring the surprising difficulty of natural yes/no questions
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On denoting
Bertrand Russell. 1905 · 1905
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 1908
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What good is an explanation?
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Bare nps: kind-referring, indefinites, both, or neither?
Manfred Krifka. 2003 · 2003
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Barbara Abbott. 2004 · 2004
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Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2004
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The penn discourse treebank
Eleni Miltsakaki, Rashmi Prasad, Aravind K Joshi, and Bonnie L Webber. 2004 · 2004
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A new look at infant pointing
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Copular clauses
Line Mikkelsen. 2011 · 2011
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Conll-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012 · 2012
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The mythos of model interpretability
Zachary C Lipton. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Make up your mind! adversarial generation of inconsistent natural language explanations
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, and Phil Blunsom. 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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Automated rationale generation: a technique for explainable ai and its effects on human perceptions
Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, and Mark O Riedl. 2019 · 2019
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Towards a rigorous science of interpretable machine learning
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End-to-end neural coreference resolution
Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 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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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 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 · 2018
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019 · 2019
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Wiqa: A dataset for “what if…” reasoning over procedural text
Niket Tandon, Bhavana Dalvi, Keisuke Sakaguchi, Peter Clark, and Antoine Bosselut. 2019 · 2019
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rstanarm: Bayesian applied regression modeling via Stan
Ben Goodrich, Jonah Gabry, Imad Ali, and Sam Brilleman. 2020 · 2020
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Nile : Natural language inference with faithful natural language explanations
Sawan Kumar and Partha Talukdar. 2020 · 2020
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Ambigqa: Answering ambiguous open-domain questions
Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020 · 2020
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Towards transparent and explainable attention models
Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M. Khapra, Balaji Vasan Srinivasan, and Balaraman Ravindran. 2020 · 2020
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Wt5?! training text-to-text models to explain their predictions
Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. 2020 · 2020
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Break it down: A question understanding benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant. 2020 · 2020
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