  There has been a rapid development in data-driven task-oriented dialogue
systems with the benefit of large-scale datasets. However, the progress of
dialogue systems in low-resource languages lags far behind due to the lack of
high-quality data. To advance the cross-lingual technology in building dialog
systems, DSTC9 introduces the task of cross-lingual dialog state tracking,
where we test the DST module in a low-resource language given the rich-resource
training dataset.
  This paper studies the transferability of a cross-lingual generative dialogue
state tracking system using a multilingual pre-trained seq2seq model. We
experiment under different settings, including joint-training or pre-training
on cross-lingual and cross-ontology datasets. We also find out the low
cross-lingual transferability of our approaches and provides investigation and
discussion.
