@inproceedings{mehri-etal-2022-lad,
    title = "{LAD}: Language Models as Data for Zero-Shot Dialog",
    author = "Mehri, Shikib  and
      Altun, Yasemin  and
      Eskenazi, Maxine",
    editor = "Lemon, Oliver  and
      Hakkani-Tur, Dilek  and
      Li, Junyi Jessy  and
      Ashrafzadeh, Arash  and
      Garcia, Daniel Hern{\'a}ndez  and
      Alikhani, Malihe  and
      Vandyke, David  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2022",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.sigdial-1.55/",
    doi = "10.18653/v1/2022.sigdial-1.55",
    pages = "595--604",
    abstract = "To facilitate zero-shot generalization in task-oriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15{\%}), slot filling (+31.4 F-1) and next action prediction (+10 F-1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs."
}