@inproceedings{li-etal-2023-shot-data,
    title = "Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning",
    author = "Li, Alexander Hanbo  and
      Shang, Mingyue  and
      Spiliopoulou, Evangelia  and
      Ma, Jie  and
      Ng, Patrick  and
      Wang, Zhiguo  and
      Min, Bonan  and
      Wang, William Yang  and
      McKeown, Kathleen  and
      Castelli, Vittorio  and
      Roth, Dan  and
      Xiang, Bing",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.894",
    doi = "10.18653/v1/2023.acl-long.894",
    pages = "16171--16189",
    abstract = "In this paper, we present a novel approach for data-to-text generation that addresses the limitations of current methods that primarily focus on specific types of structured data. Our proposed method aims to improve performance in multi-task training, zero-shot and few-shot scenarios by providing a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. We demonstrate that our proposed approach can effectively adapt to new structured forms, and can improve performance in comparison to current methods. For example, our method resulted in a 66{\%} improvement in zero-shot BLEU scores when transferring models trained on table inputs to a knowledge graph dataset. Our proposed method is an important step towards a more general data-to-text generation framework.",
}

In this paper, we present a novel approach for data-to-text generation that addresses the limitations of current methods that primarily focus on specific types of structured data. Our proposed method aims to improve performance in multi-task training, zero-shot and few-shot scenarios by providing a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. We demonstrate that our proposed approach can effectively adapt to new structured forms, and can improve performance in comparison to current methods. For example, our method resulted in a 66{\%} improvement in zero-shot BLEU scores when transferring models trained on table inputs to a knowledge graph dataset. Our proposed method is an important step towards a more general data-to-text generation framework.
Li, Alex er Hanbo Shang, Mingyue Spiliopoulou, Evangelia Ma, Jie Ng, Patrick Wang, Zhiguo Min, Bonan Wang, William Yang McKeown, Kathleen Castelli, Vittorio Roth, Dan Xiang, Bing
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning