Natural Language Generation (NLG) plays a critical role in Spoken Dialogue Systems (SDSs), aims at converting a meaning representation into natural language utterances. Recent deep learning-based generators have shown improving results irrespective of providing sufficient annotated data. Nevertheless, how to build a generator that can effectively utilize as much of knowledge from a low-resource setting data is a crucial issue for NLG in SDSs. This paper presents a variational-based NLG framework to tackle the NLG problem of having limited annotated data in two scenarios, domain adaptation and low-resource in-domain training data. Based on this framework, we propose a novel adversarial domain adaptation NLG taclking the former issue, while the latter issue is also handled by a second proposed dual variational model. We extensively conducted the experiments on four different domains in a variety of training scenarios, in which the experimental results show that the proposed methods not only outperform previous methods when having sufficient training dataset but also show its ability to work acceptably well when there is a small amount of in-domain data or adapt quickly to a new domain with only a low-resource target domain data.

自然语言生成（NLG）在口语对话系统（SDS）中扮演着至关重要的角色，目的是将含义表示转换为自然语言。最近的基于深度学习的生成器已显示出改善的结果，而与提供足够的带注释数据无关。然而，对于SDS中的NLG而言，如何构建一种能够有效利用资源匮乏设置数据中的大量知识的生成器是至关重要的问题。本文提出了一种基于变体的NLG框架，以解决在两种情况下（域适应和低资源域内训练数据）具有有限注释数据的NLG问题。在此框架的基础上，我们提出了一种新颖的对抗域自适应神经网络，解决了前者的问题，而后者也由第二个提出的双重变分模型处理。