Recent progress in task-oriented neural dialogue systems is largely focused
on a handful of languages, as annotation of training data is tedious and
expensive. Machine translation has been used to make systems multilingual, but
this can introduce a pipeline of errors. Another promising solution is using
cross-lingual transfer learning through pretrained multilingual models.
Existing methods train multilingual models with additional code-mixed task data
or refine the cross-lingual representations through parallel ontologies. In
this work, we enhance the transfer learning process by intermediate fine-tuning
of pretrained multilingual models, where the multilingual models are fine-tuned
with different but related data and/or tasks. Specifically, we use parallel and
conversational movie subtitles datasets to design cross-lingual intermediate
tasks suitable for downstream dialogue tasks. We use only 200K lines of
parallel data for intermediate fine-tuning which is already available for 1782
language pairs. We test our approach on the cross-lingual dialogue state
tracking task for the parallel MultiWoZ (English -> Chinese, Chinese ->
English) and Multilingual WoZ (English -> German, English -> Italian) datasets.
We achieve impressive improvements (> 20% on joint goal accuracy) on the
parallel MultiWoZ dataset and the Multilingual WoZ dataset over the vanilla
baseline with only 10% of the target language task data and zero-shot setup
respectively.

该论文提出了一种在预训练多语言模型之间进行跨语言传递学习的方法，通过中间微调预训练的多语言模型，使其适用于不同但相关的数据和 / 或任务，以提高对话系统的性能。