Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity

Understand

End-to-end neural data-to-text (D2T) generation has recently emerged as an alternative to pipeline-based architectures.

• However, it has faced challenges in generalizing to new domains and generating semantically consistent text.
• In this work, we present DataTuner, a neural, end-to-end data-to-text generation system that makes minimal assumptions about the data representation and the target domain.
• We take a two-stage generation-reranking approach, combining a fine-tuned language model with a semantic fidelity classifier.