Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
This work addresses the challenge of semantic fidelity in data-to-text generation for applications requiring accurate and fluent text output, representing a strong specific gain rather than a foundational breakthrough.
The authors tackled the problem of generating semantically consistent text from data in end-to-end neural data-to-text generation, achieving state-of-the-art results on automated metrics across four datasets and generating text with significantly better semantic fidelity.
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