Collaborative Neural-Symbolic Rule Generation for Explainable Data-to-Text Systems
An extension of the Data2Text framework is presented that introduces a collaborative neural-symbolic approach to explainable data-to-text generation that reduces manual rule engineering effort, increases linguistic variability, and supports adaptation to new domains and languages, while maintaining full explainability.
G. Simonazzi, A. Dal Palù
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