Dialogue systems for non-English languages have long been under-explored. In this paper, we take the first step to investigate few-shot cross-lingual transfer learning (FS-XLT) and multitask learning (MTL) in the context of open-domain dialogue generation for non-English languages with limited data. We observed catastrophic forgetting in both FS-XLT and MTL for all 6 languages in our preliminary experiments. To mitigate the issue, we propose a simple yet effective prompt learning approach that can preserve the multilinguality of multilingual pre-trained language model (mPLM) in FS-XLT and MTL by bridging the gap between pre-training and fine-tuning with Fixed-prompt LM Tuning and our hand-crafted prompts. Experimental results on all 6 languages in terms of both automatic and human evaluations demonstrate the effectiveness of our approach. Our code is available at https://github.com/JeremyLeiLiu/XLinguDial.

翻译：非英语语言的对话系统长期未得到充分探索。本文首次研究在数据有限的非英语语言中，面向开放域对话生成的少样本跨语言迁移学习（FS-XLT）与多任务学习（MTL）。初步实验观察到，在FS-XLT和MTL中对所有6种语言均存在灾难性遗忘现象。为缓解该问题，我们提出一种简单而有效的提示学习方法，通过固定提示语言模型微调（Fixed-prompt LM Tuning）与手工设计的提示，弥合预训练与微调之间的差距，从而在多语言预训练语言模型（mPLM）的FS-XLT与MTL中保留其多语言特性。在6种语言上的自动评估与人工评估结果均证明了该方法的有效性。代码已开源：https://github.com/JeremyLeiLiu/XLinguDial。