@inproceedings{osuji-etal-2024-dcu,
    title = "{DCU}-{ADAPT}-mod{PB} at the {GEM}{'}24 Data-to-Text Generation Task: Model Hybridisation for Pipeline Data-to-Text Natural Language Generation",
    author = "Osuji, Chinonso Cynthia  and
      Huidrom, Rudali  and
      Adebayo, Kolawole John  and
      Castro Ferreira, Thiago  and
      Davis, Brian",
    editor = "Mille, Simon  and
      Clinciu, Miruna-Adriana",
    booktitle = "Proceedings of the 17th International Natural Language Generation Conference: Generation Challenges",
    month = sep,
    year = "2024",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.inlg-genchal.7/",
    pages = "66--75",
    abstract = "In this paper, we present our approach to the GEM Shared Task at the INLG{'}24 Generation Challenges, which focuses on generating data-to-text in multiple languages, including low-resource languages, from WebNLG triples. We employ a combination of end-to-end and pipeline neural architectures for English text generation. To extend our methodology to Hindi, Korean, Arabic, and Swahili, we leverage a neural machine translation model. Our results demonstrate that our approach achieves competitive performance in the given task."
}