Data-to-Text Generation Improves Decision-Making Under Uncertainty

Gkatzia, Dimitra; Lemon, Oliver; Rieser, Verena

Abstract

Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. This article presents a comparison of different information presentations for uncertain data and, for the first time, measures their effects on human decision-making, in the domain of weather forecast generation. We use a game-based setup to evaluate the different systems. We show that the use of Natural Language Generation (NLG) enhances decision-making under uncertainty, compared to state-of-the-art graphical-based representation methods.
In a task-based study with 442 adults, we found that presentations using NLG led to 24% better decision-making on average than the graphical presentations, and to 44% better decision-making when NLG is combined with graphics. We also show that women achieve significantly better results when presented with NLG output (an 87% increase on average compared to graphical presentations). Finally, we present a further analysis of demographic data and its impact on decision-making, and we discuss implications for future NLG systems.

Citation

Gkatzia, D., Lemon, O., & Rieser, V. (2017). Data-to-Text Generation Improves Decision-Making Under Uncertainty. IEEE Computational Intelligence Magazine, 12(3), 10-17. https://doi.org/10.1109/MCI.2017.2708998

• Journal Article Type: Article
• Acceptance Date: Feb 18, 2017
• Online Publication Date: Jul 18, 2017
• Publication Date: 2017-08
• Deposit Date: Feb 21, 2017
• Publicly Available Date: Apr 28, 2017
• Journal: IEEE Computational Intelligence Magazine
• Print ISSN: 1556-603X
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 3
• Pages: 10-17
• DOI: https://doi.org/10.1109/MCI.2017.2708998
• Keywords: Natural language processing, Decision making, Data analysis, Games, Pragmatics, Uncertainty, Weather forecasting
• Public URL: http://researchrepository.napier.ac.uk/Output/687579
• Contract Date: Apr 24, 2017