  This paper describes our approach to DSTC 9 Track 2: Cross-lingual
Multi-domain Dialog State Tracking, the task goal is to build a Cross-lingual
dialog state tracker with a training set in rich resource language and a
testing set in low resource language. We formulate a method for joint learning
of slot operation classification task and state tracking task respectively.
Furthermore, we design a novel mask mechanism for fusing contextual information
about dialogue, the results show the proposed model achieves excellent
performance on DSTC Challenge II with a joint accuracy of 62.37% and 23.96% in
MultiWOZ(en - zh) dataset and CrossWOZ(zh - en) dataset, respectively.
