  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.
