@inproceedings{calo-etal-2025-incorporating,
    title = "Incorporating Formulaicness in the Automatic Evaluation of Naturalness: A Case Study in Logic-to-Text Generation",
    author = "Cal{\`o}, Eduardo  and
      Chen, Guanyi  and
      Stengel-Eskin, Elias  and
      Gatt, Albert  and
      van Deemter, Kees",
    editor = "Flek, Lucie  and
      Narayan, Shashi  and
      Phương, L{\^e} Hồng  and
      Pei, Jiahuan",
    booktitle = "Proceedings of the 18th International Natural Language Generation Conference",
    month = oct,
    year = "2025",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-you-zhang-rochester/2025.inlg-main.21/",
    pages = "352--365",
    abstract = "Data-to-text natural language generation (NLG) models may produce outputs that closely mirror the structure of their input. We introduce formulaicness as a measure of the output-to-input structural resemblance, proposing it as an enhancement for reference-less naturalness evaluation. Focusing on logic-to-text generation, we construct a dataset and train a regressor to predict formulaicness scores. We collect human judgments on naturalness and examine how incorporating formulaicness into existing metrics affects alignment with these judgments."
}