  Despite the surging demands for multilingual task-oriented dialog systems
(e.g., Alexa, Google Home), there has been less research done in multilingual
or cross-lingual scenarios. Hence, we propose a zero-shot adaptation of
task-oriented dialogue system to low-resource languages. To tackle this
challenge, we first use a set of very few parallel word pairs to refine the
aligned cross-lingual word-level representations. We then employ a latent
variable model to cope with the variance of similar sentences across different
languages, which is induced by imperfect cross-lingual alignments and inherent
differences in languages. Finally, the experimental results show that even
though we utilize much less external resources, our model achieves better
adaptation performance for natural language understanding task (i.e., the
intent detection and slot filling) compared to the current state-of-the-art
model in the zero-shot scenario.
