Research Output
Data-to-Text Generation Improves Decision-Making Under Uncertainty
  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.

  • Type:

    Article

  • Date:

    18 July 2017

  • Publication Status:

    Published

  • DOI:

    10.1109/MCI.2017.2708998

  • ISSN:

    1556-603X

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    Engineering and Physical Sciences Research Council

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

Authors

Keywords

Natural language processing, Decision making, Data analysis, Games, Pragmatics, Uncertainty, Weather forecasting

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