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

Dimitra Gkatzia, Oliver Lemon, Verena Rieser

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
199 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)10-17
Number of pages8
JournalIEEE Computational Intelligence Magazine
Issue number3
Early online date18 Jul 2017
Publication statusPublished - Aug 2017


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