Data-to-Text Generation Improves Decision-Making Under Uncertainty
- Submitting institution
-
Edinburgh Napier University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1110866
- Type
- D - Journal article
- DOI
-
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
-
https://github.com/dimi123/WeatherGame
- 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
- The paper establishes that information from uncertain data is better absorbed when shown through language and visuals, rather than just visually as was the case before that. The research has been featured in a blog: https://understandinguncertainty.org/women-listen-and-men-look-how-best-communicate-risk-support-decision-making This paper (and its shorter version) have led to an ogoing discussion in NLG on how task based evaluations can be used for the challenging topic of evaluating generated language.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -