Improving Data-to-Text Generation via Preserving High-Frequency Phrases and Fact-Checking
Transforming numerical data into natural language descriptions (data-to-text) requires presenting the data in the correct context, supplementing plausible details, and creating an overall coherent and non-conflicting narrative. In this work, we propose a generate-extract-correct pipeline for the tas...
Enregistré dans:
| Dans: | URI:https://journals.openedition.org/ijcol, |
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| Auteurs principaux: | , , |
| Format: | Article ou chapitre numérique |
| Langue: | Anglais |
| Publié: |
IJCoL
2021
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| Accès en ligne: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |