@inproceedings{chandu-etal-2023-lowrecorp,
    title = "{LOWRECORP}: the Low-Resource {NLG} Corpus Building Challenge",
    author = "Chandu, Khyathi Raghavi  and
      Howcroft, David M.  and
      Gkatzia, Dimitra  and
      Chung, Yi-Ling  and
      Hou, Yufang  and
      Emezue, Chris Chinenye  and
      Rajpoot, Pawan  and
      Adewumi, Tosin",
    editor = "Mille, Simon",
    booktitle = "Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges",
    month = sep,
    year = "2023",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.inlg-genchal.1",
    pages = "1--9",
    abstract = "Most languages in the world do not have sufficient data available to develop neural-network-based natural language generation (NLG) systems. To alleviate this resource scarcity, we propose a novel challenge for the NLG community: low-resource language corpus development (LOWRECORP). We present an innovative framework to collect a single dataset with dual tasks to maximize the efficiency of data collection efforts and respect language consultant time. Specifically, we focus on a text-chat-based interface for two generation tasks {--} conversational response generation grounded in a source document and/or image and dialogue summarization (from the former task). The goal of this shared task is to collectively develop grounded datasets for local and low-resourced languages. To enable data collection, we make available web-based software that can be used to collect these grounded conversations and summaries. Submissions will be assessed for the size, complexity, and diversity of the corpora to ensure quality control of the datasets as well as any enhancements to the interface or novel approaches to grounding conversations.",
}