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Found 4 papers, 1 papers with code
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing
A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data.
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
Our generated text has a significantly better semantic fidelity than the state of the art across all four datasets
Ranked #1 on
Data-to-Text Generation
on ViGGO
Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts
The current most popular method for automatic Natural Language Generation (NLG) evaluation is comparing generated text with human-written reference sentences using a metrics system, which has drawbacks around reliability and scalability.
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