Papers › Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
Hamza Harkous, Isabel Groves, Amir Saffari
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. Each of our components is learnt end-to-end without the need for dataset-specific heuristics, entity delexicalization, or post-processing. We show that DataTuner achieves state of the art results on the automated metrics across four major D2T datasets (LDC2017T10, WebNLG, ViGGO, and Cleaned E2E), with a fluency assessed by human annotators nearing or exceeding the human-written reference texts. We further demonstrate that the model-based semantic fidelity scorer in DataTuner is a better assessment tool compared to traditional, heuristic-based measures. Our generated text has a significantly better semantic fidelity than the state of the art across all four datasets
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Data-to-Text Generation | Cleaned E2E NLG Challenge | DataTuner_FC | BLEU (Test set) | 43.6 | #2 of 7 | Archive leaderboard | report |
| Data-to-Text Generation | ViGGO | DataTuner_FC | BLEU | 53.6 | #1 of 2 | Archive leaderboard | report |
| Data-to-Text Generation | WebNLG Full | DATATUNER_NO_FC | BLEU | 52.9 | #7 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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