If it may have happened before, it happened, but not necessarily before

Temporal uncertainty in raw data can impede the inference of temporal and causal relationships between events and compromise the output of data-to-text NLG systems. In this paper, we introduce a framework to reason with and represent temporal uncertainty from the raw data to the generated text, in order to provide a faithful picture to the user of a particular situation. The model is grounded in experimental data from multiple languages, shedding light on the generality of the approach.peer-reviewe

If it may have happened before, it happened, but not necessarily before

Temporal uncertainty in raw data can impede the inference of temporal and causal relationships between events and compromise the output of data-to-text NLG systems. In this paper, we introduce a framework to reason with and represent temporal uncertainty from the raw data to the generated text, in order to provide a faithful picture to the user of a particular situation. The model is grounded in experimental data from multiple languages, shedding light on the generality of the approach.

Task based model for récit generation from sensor data: an early experiment

International audienceAutomatic story generation is the object of a growing research effort in Computing Sciences. However, in this domain, stories are generally produced from fictional data. In this paper, we present the general approach for automatic story generation from real data focusing on the narrative planning. The aim is to generate récit from sensor observations of skiers going for a ski sortie. The modelling of the récit as well as some preliminary experiments are introduced and suggest the interest of the approach

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