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.
@article{arxiv.2106.14433,
title = {Efficient Dialogue State Tracking by Masked Hierarchical Transformer},
author = {Min Mao and Jiasheng Liu and Jingyao Zhou and Haipang Wu},
journal= {arXiv preprint arXiv:2106.14433},
year = {2021}
}