Statements (46)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:large_language_model
|
| gptkbp:application |
Sports Reporting
Automated Journalism Business Intelligence Reporting Financial Report Generation Weather Forecast Generation |
| gptkbp:assesses |
gptkb:ROUGE
gptkb:METEOR gptkb:BLEU Human Evaluation |
| gptkbp:challenge |
gptkb:Coherence
Data Interpretation Fluency Content Selection Maintaining Factual Accuracy Surface Realization |
| gptkbp:field |
gptkb:artificial_intelligence
gptkb:Natural_Language_Processing |
| gptkbp:firstResearch |
1980s
|
| gptkbp:input |
Structured Data
|
| gptkbp:method |
Statistical Methods
Neural Network-based Generation Rule-based Generation Template-based Generation |
| gptkbp:notableFor |
AX Semantics
Arria NLG Quill (Narrative Science) Yseop |
| gptkbp:output |
Natural Language Text
|
| gptkbp:relatedConcept |
gptkb:Text_Summarization
Data-to-Text Systems Text-to-Text Generation |
| gptkbp:surveyPaper |
Gatt, A., & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation.
Gkatzia, D. (2016). Data-to-text generation: a survey. Reiter, E., & Dale, R. (2000). Building Natural Language Generation Systems. |
| gptkbp:trainer |
gptkb:WebNLG
gptkb:Rotowire E2E NLG Challenge WeatherGov WikiBio |
| gptkbp:usedIn |
Automated Email Generation
Customer Communication E-commerce Product Descriptions Healthcare Reporting Personalized Content Generation |
| https://www.w3.org/2000/01/rdf-schema#label |
Data-to-Text Generation
|