Building dialogue generation systems in a zero-shot scenario remains a huge
challenge, since the typical zero-shot approaches in dialogue generation rely
heavily on large-scale pre-trained language generation models such as GPT-3 and
T5. The research on zero-shot dialogue generation without cumbersome language
models is limited due to lacking corresponding parallel dialogue corpora. In
this paper, we propose a simple but effective Multilingual learning framework
for Zero-shot Dialogue Generation (dubbed as MulZDG) that can effectively
transfer knowledge from an English corpus with large-scale training samples to
a non-English corpus with zero samples. Besides, MulZDG can be viewed as a
multilingual data augmentation method to improve the performance of the
resource-rich language. First, we construct multilingual code-switching
dialogue datasets via translation utterances randomly selected from monolingual
English datasets. Then we employ MulZDG to train a unified multilingual
dialogue model based on the code-switching datasets. The MulZDG can conduct
implicit semantic alignment between different languages. Experiments on
DailyDialog and DSTC7 datasets demonstrate that MulZDG not only achieve
competitive performance under zero-shot case compared to training with
sufficient examples but also greatly improve the performance of the source
language.

本文提出了一种名为 MulZDG 的多语言学习框架，它利用从英语语料库中抽取的翻译话语来构建多语言代码转换对话数据集，并通过 MulZDG 来训练一个统一的多语言对话模型，可以实现不同语言之间的隐式语义对齐，对零样本对话生成和资源丰富的语言表现出竞争性的性能和显著的性能提升。