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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Accademia University Press
2021-12-01
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| Series: | IJCoL |
| Online Access: | https://journals.openedition.org/ijcol/909 |
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