@inproceedings{howcroft-etal-2024-exploring-impact,
    title = "Exploring the impact of data representation on neural data-to-text generation",
    author = "Howcroft, David M.  and
      Watson, Lewis N.  and
      Nedopas, Olesia  and
      Gkatzia, Dimitra",
    editor = "Mahamood, Saad  and
      Minh, Nguyen Le  and
      Ippolito, Daphne",
    booktitle = "Proceedings of the 17th International Natural Language Generation Conference",
    month = sep,
    year = "2024",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.inlg-main.20/",
    pages = "243--253",
    abstract = "A relatively under-explored area in research on neural natural language generation is the impact of the data representation on text quality. Here we report experiments on two leading input representations for data-to-text generation: attribute-value pairs and Resource Description Framework (RDF) triples. Evaluating the performance of encoder-decoder seq2seq models as well as recent large language models (LLMs) with both automated metrics and human evaluation, we find that the input representation does not seem to have a large impact on the performance of either purpose-built seq2seq models or LLMs. Finally, we present an error analysis of the texts generated by the LLMs and provide some insights into where these models fail."
}