论文标题
LAD：语言模型作为零击对话框的数据
LAD: Language Models as Data for Zero-Shot Dialog
论文作者
论文摘要
为了促进零弹性对话框中的零拍，本文提出语言模型作为数据（LAD）。 LAD是创建各种准确的合成数据的范式，该数据传达了必要的结构约束，可用于训练下游神经对话模型。 LAD利用GPT-3诱导语言多样性。 LAD在意图预测（+15％），插槽填充（+31.4 f-1）和下一个动作预测（+11 F1）上，在零拍设置中获得了显着的性能提高。此外，交互式人类评估表明，与LAD的培训具有在人类对话中培训的竞争力。 LAD是开源的，可在https://github.com/shikib/lad上获得代码和数据。
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 (+31.4 F-1) and next action prediction (+11 F1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs. LAD is open-sourced, with the code and data available at https://github.com/Shikib/lad.
