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End-to-end neural data-to-text (D2T) generation has recently emerged as analternative to pipeline-based architectures. However, it has faced challengesin generalizing to new domains and generating semantically consistent text. Inthis work, we present DataTuner, a neural, end-to-end data-to-text generationsystem that makes minimal assumptions about the data representation and thetarget domain. We take a two-stage generation-reranking approach, combining afine-tuned language model with a semantic fidelity classifier. Each of ourcomponents is learnt end-to-end without the need for dataset-specificheuristics, entity delexicalization, or post-processing. We show that DataTunerachieves state of the art results on the automated metrics across four majorD2T datasets (LDC2017T10, WebNLG, ViGGO, and Cleaned E2E), with a fluencyassessed by human annotators nearing or exceeding the human-written referencetexts. We further demonstrate that the model-based semantic fidelity scorer inDataTuner is a better assessment tool compared to traditional, heuristic-basedmeasures. Our generated text has a significantly better semantic fidelity thanthe state of the art across all four datasets

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