  Much recent progress in task-oriented dialogue (ToD) systems has been driven
by available annotation data across multiple domains for training. Over the
last few years, there has been a move towards data curation for multilingual
ToD systems that are applicable to serve people speaking different languages.
However, existing multilingual ToD datasets either have a limited coverage of
languages due to the high cost of data curation, or ignore the fact that
dialogue entities barely exist in countries speaking these languages. To tackle
these limitations, we introduce a novel data curation method that generates
GlobalWoZ -- a large-scale multilingual ToD dataset globalized from an English
ToD dataset for three unexplored use cases. Our method is based on translating
dialogue templates and filling them with local entities in the target-language
countries. We release our dataset as well as a set of strong baselines to
encourage research on learning multilingual ToD systems for real use cases.
