LAD: Language Models as Data for Zero-Shot Dialog
Shikib Mehri, Yasemin Altün, Maxine Eskénazi · 2022
To facilitate zero-shot generalization in taskoriented 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 (+31.4F-1) and next action prediction (+11 F-1).Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs.