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Good quality explanations of artificial intelligence (XAI) reasoning must be written (and evaluated) for an explanatory purpose, targeted towards their readers, have a good narrative and causal structure, and highlight where uncertainty and data quality affect the AI output.
Discovery evaluation report: Theta vest structure
Statoil. 1993 · 1993
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Building natural language generation systems
Ehud Reiter and Robert Dale. 2000 · 2000
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A review of explanation methods for Bayesian networks
Carmen Lacave and Francisco J Díez. 2002 · 2002
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Risk, communication and health psychology
Dianne Berry. 2004 · 2004
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Generation and evaluation of user tailored responses in multimodal dialogue
M.A. Walker, S.J. Whittaker, A. Stent, P. Maloor, J. Moore, M. Johnston, and G. Vasireddy. 2004 · 2004
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The importance of narrative and other lessons from an evaluation of an NLG system that summarises clinical data
Ehud Reiter, Albert Gatt, François Portet, and Marian Van Der Meulen. 2008 · 2008
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Automatic generation of textual summaries from neonatal intensive care data
François Portet, Ehud Reiter, Albert Gatt, Jim Hunter, Somayajulu Sripada, Yvonne Freer, and Cindy Sykes. 2009 · 2009
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Thinking, fast and slow
Daniel Kahneman. 2011 · 2011
Cited alongside, same era.
Generating affective natural language for parents of neonatal infants
Saad Mahamood and Ehud Reiter. 2011 · 2011
Cited alongside, same era.
Deep water: the gulf oil disaster and the future of offshore drilling: report to the president
NCBPDeepwaterHorizonSpill. 2011 · 2011
Cited alongside, same era.
Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse
James Hunter, Yvonne Freer, Albert Gatt, Ehud Reiter, Somayajulu Sripada, and Cindy Sykes. 2012 · 2012
Cited alongside, same era.
Evaluating the effectiveness of explanations for recommender systems
Nava Tintarev and Judith Masthoff. 2012 · 2012
Cited alongside, same era.
Not exactly: In praise of vagueness
Kees Van Deemter. 2012 · 2012
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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An exploratory study on the benefits of using natural language for explaining fuzzy rule-based systems
Jose M Alonso, Alejandro Ramos-Soto, Ehud Reiter, and Kees van Deemter. 2017 · 2017
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Explanation and justification in machine learning: A survey
Or Biran and Courtenay Cotton. 2017 · 2017
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Human-centric justification of machine learning predictions
Or Biran and Kathleen R McKeown. 2017 · 2017
Later among the works it cites.
Textually summarising incomplete data
Stephanie Inglis, Ehud Reiter, and Somayajulu Sripada. 2017 · 2017
Later among the works it cites.
Survey of the state of the art in natural language generation: Core tasks, applications and evaluation
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Cited alongside, same era.
A snapshot of NLG evaluation practices 2005-2014
Dimitra Gkatzia and Saad Mahamood. 2015 · 2014
Cited alongside, same era.
User modelling in text generation
Cecile Paris. 2015 · 2015
Cited alongside, same era.
Albert Gatt and Emiel Krahmer. 2018 · 2018
Later among the works it cites.
Explaining explanations: An overview of interpretability of machine learning
Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. 2018 · 2018
Later among the works it cites.
Risk communication: A handbook for communicating environmental, safety, and health risks
Regina E Lundgren and Andrea H McMakin. 2018 · 2018
Later among the works it cites.