@inproceedings{aditya-hari-etal-2023-webnlg,
    title = "{W}eb{NLG} Challenge 2023: Domain Adaptive Machine Translation for Low-Resource Multilingual {RDF}-to-Text Generation ({W}eb{NLG} 2023)",
    author = "Aditya Hari, Kancharla  and
      Singh, Bhavyajeet  and
      Sharma, Anubhav  and
      Varma, Vasudeva",
    editor = "Gatt, Albert  and
      Gardent, Claire  and
      Cripwell, Liam  and
      Belz, Anya  and
      Borg, Claudia  and
      Erdem, Aykut  and
      Erdem, Erkut",
    booktitle = "Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)",
    month = sep,
    year = "2023",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2023.mmnlg-1.11/",
    pages = "93--94",
    abstract = "This paper presents our submission to the WebNLG Challenge 2023 for generating text in several low-resource languages from RDF-triples. Our submission focuses on using machine translation for generating texts in Irish, Maltese, Welsh and Russian. While a simple and straightfoward approach, recent works have shown that using monolingual models for inference for multilingual tasks with the help of machine translation (translate-test) can out-perform multilingual models and training multilingual models on machine-translated data (translate-train) through careful tuning of the MT component. Our results show that this approach demonstrates competitive performance for this task even with limited data."
}