Domain Adaptation arises when we aim at learning from source domain a model
that can per- form acceptably well on a different target domain. It is
especially crucial for Natural Language Generation (NLG) in Spoken Dialogue
Systems when there are sufficient annotated data in the source domain, but
there is a limited labeled data in the target domain. How to effectively
utilize as much of existing abilities from source domains is a crucial issue in
domain adaptation. In this paper, we propose an adversarial training procedure
to train a Variational encoder-decoder based language generator via multiple
adaptation steps. In this procedure, a model is first trained on a source
domain data and then fine-tuned on a small set of target domain utterances
under the guidance of two proposed critics. Experimental results show that the
proposed method can effec- tively leverage the existing knowledge in the source
domain to adapt to another related domain by using only a small amount of
in-domain data.

本研究提出了一种通过对抗训练方法训练基于变分编码器 - 解码器的语言生成器来实现域适应的方法，通过该方法，可以仅使用少量的领域内数据就能够有效地利用源领域中现有的知识来适应其他相关领域。

对话系统中基于变分神经语言生成的对抗领域自适应

Adversarial Domain Adaptation for Variational Neural Language Generation  in Dialogue Systems

The variational encoder-decoder (VED) encodes source information as a set of
random variables using a neural network, which in turn is decoded into target
data using another neural network. In natural language processing,
sequence-to-sequence (Seq2Seq) models typically serve as encoder-decoder
networks. When combined with a traditional (deterministic) attention mechanism,
the variational latent space may be bypassed by the attention model, and thus
becomes ineffective. In this paper, we propose a variational attention
mechanism for VED, where the attention vector is also modeled as Gaussian
distributed random variables. Results on two experiments show that, without
loss of quality, our proposed method alleviates the bypassing phenomenon as it
increases the diversity of generated sentences.

本文提出了一种变分注意力机制来避免变分编码器 - 解码器中的未被注意模型绕过，并在增加生成语句的多样性的同时不降低其质量。