Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data
TABLE II: Validation questionnaire score
['Questions', 'Average score', 'Standard deviation']
Based on the results obtained for the polishing stage, we have improved the NLG modules to address the issues found in our first approach and a validation test has been performed by the meteorologist with new 45 automatically generated natural language forecasts. In particular, the results in Question 1 show that the expert fully identifies the automatically generated forecasts as if they were produced manually by him. The fact that both content and language from the automatic forecasts are almost indistinguishable from those that an expert would produce are the most important among the several quality aspects which can be measured for a NLG approach. The remaining Questions also show increased scores compared to the first assessment.