Natural language generation (NLG) is an essential component of task-oriented
dialogue systems. Despite the recent success of neural approaches for NLG, they
are typically developed for particular domains with rich annotated training
examples. In this paper, we study NLG in a low-resource setting to generate
sentences in new scenarios with handful training examples. We formulate the
problem from a meta-learning perspective, and propose a generalized
optimization-based approach (Meta-NLG) based on the well-recognized
model-agnostic meta-learning (MAML) algorithm. Meta-NLG defines a set of meta
tasks, and directly incorporates the objective of adapting to new low-resource
NLG tasks into the meta-learning optimization process. Extensive experiments
are conducted on a large multi-domain dataset (MultiWoz) with diverse
linguistic variations. We show that Meta-NLG significantly outperforms other
training procedures in various low-resource configurations. We analyze the
results, and demonstrate that Meta-NLG adapts extremely fast and well to
low-resource situations.

本文探讨了在低资源环境下生成新场景的句子，提出了基于元学习的通用优化方法（Meta-NLG）来解决这个问题，并在大型多域数据集上进行了实验，表明 Meta-NLG 在各种低资源配置中显著优于其他训练过程，适应低资源情况极快且良好。