@inproceedings{perez-beltrachini-etal-2016-building,
    title = "Building {RDF} Content for Data-to-Text Generation",
    author = "Perez-Beltrachini, Laura  and
      Sayed, Rania  and
      Gardent, Claire",
    editor = "Matsumoto, Yuji  and
      Prasad, Rashmi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/C16-1141/",
    pages = "1493--1502",
    abstract = "In Natural Language Generation (NLG), one important limitation is the lack of common benchmarks on which to train, evaluate and compare data-to-text generators. In this paper, we make one step in that direction and introduce a method for automatically creating an arbitrary large repertoire of data units that could serve as input for generation. Using both automated metrics and a human evaluation, we show that the data units produced by our method are both diverse and coherent."
}