Generating Numerical Approximations

Power, Richard; Williams, Sandra

doi:10.1162/coli_a_00086

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…Kennedy & McNally, 2005;van Deemter, 2012). A similar issue has been noted in the context of presenting numerical information, such as timestamps and quantities Power & Williams, 2012). For example, Reiter et al (2005) discussed time expressions in the context of weather-forecast generation, pointing out that a timestamp 00:00 could be expressed as late evening, midnight, or simply evening (Reiter et al, 2005, p. 143).…”

Section: Lexicalisationmentioning

confidence: 79%

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

Gatt¹,

Krahmer²

2018

jair

617

397

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This paper surveys the current state of the art in Natural Language Generation (nlg), defined as the task of generating text or speech from non-linguistic input. A survey of nlg is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of nlg technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in nlg and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between nlg and other areas of artificial intelligence; (c) draw attention to the challenges in nlg evaluation, relating them to similar challenges faced in other areas of nlp, with an emphasis on different evaluation methods and the relationships between them.

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Section: Lexicalisationmentioning

confidence: 79%

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

Gatt¹,

Krahmer²

2018

jair

617

397

View full text Add to dashboard Cite

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“…For generation, they train a predictive model to select expressions to convey uncertainty based on human data. [22] present recent work on automatically translating numbers into NL expressions, using a constraint-based framework for verbalizing numbers (proportions) at different levels of precision.…”

Section: A Natural Language Generationmentioning

confidence: 99%

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

Gkatzia

Lemon

Rieser

2017

IEEE Comput. Intell. Mag.

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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.

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“…These include, for instance, the proposals by van Deemter, where the use of referring expressions involving gradable properties [73] and the practical implications of vague expressions in NLG are explored [74]; an approach by Power and Williams which deals with numerical approximations to describe proportions at different levels of precision [75]; and the extension of the data interpretation and microplanning stages in the BABYTALK family of systems to deal with uncertainty in temporal relations, by Portet and Gatt in [76].…”

Section: Remarksmentioning

confidence: 99%