@inproceedings{puzikov-gurevych-2018-e2e,
    title = "{E}2{E} {NLG} Challenge: Neural Models vs. Templates",
    author = "Puzikov, Yevgeniy  and
      Gurevych, Iryna",
    editor = "Krahmer, Emiel  and
      Gatt, Albert  and
      Goudbeek, Martijn",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W18-6557/",
    doi = "10.18653/v1/W18-6557",
    pages = "463--471",
    abstract = "E2E NLG Challenge is a shared task on generating restaurant descriptions from sets of key-value pairs. This paper describes the results of our participation in the challenge. We develop a simple, yet effective neural encoder-decoder model which produces fluent restaurant descriptions and outperforms a strong baseline. We further analyze the data provided by the organizers and conclude that the task can also be approached with a template-based model developed in just a few hours."
}