Data-to-Text Generation Improves Decision-Making Under Uncertainty
- Submitting institution
-
Heriot-Watt University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 15703930
- Type
- D - Journal article
- DOI
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10.1109/MCI.2017.2708998
- Title of journal
- IEEE Computational Intelligence Magazine
- Article number
- -
- First page
- 10
- Volume
- 12
- Issue
- 3
- ISSN
- 1556-603X
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Paper is the first to show that Natural Language Generation (NLG) systems can increase the quality and confidence of human decision making when the decision maker is faced with uncertainty. This is a multi-disciplinary research topic, the results have major implications for designing decision support systems and human-robot interaction as evidenced by interdisciplinary citations. Note that most citations are to a shorter version of paper, which was published at the premier venue in the field (ACL'16). This research led to several invited events e.g. European Research Night 2016, Edinburgh Science Festival, 2017, guest contribution to Prof. D.Spiegelhalter@statslab.cam.ac.uk 's blog "understandinguncertainty.org".
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
