@inproceedings{pasricha-etal-2020-utilising,
    title = "Utilising Knowledge Graph Embeddings for Data-to-Text Generation",
    author = "Pasricha, Nivranshu  and
      Arcan, Mihael  and
      Buitelaar, Paul",
    editor = "Castro Ferreira, Thiago  and
      Gardent, Claire  and
      Ilinykh, Nikolai  and
      van der Lee, Chris  and
      Mille, Simon  and
      Moussallem, Diego  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.webnlg-1.6/",
    pages = "48--53",
    abstract = "Data-to-text generation has recently seen a move away from modular and pipeline architectures towards end-to-end architectures based on neural networks. In this work, we employ knowledge graph embeddings and explore their utility for end-to-end approaches in a data-to-text generation task. Our experiments show that using knowledge graph embeddings can yield an improvement of up to 2 {--} 3 BLEU points for seen categories on the WebNLG corpus without modifying the underlying neural network architecture."
}