Microsoft Research Asia's Systems for WMT19

Yingce Xia, Xu T an, Fei Tian, Fei Gao, Weicong Chen, Y ang Fan, Linyuan Gong, Yichong Leng, Renqian Luo, Yiren Wang, Lijun Wu, Jinhua Zhu, T ao Qin, Tie-Y an Liu Microsoft Research Asia Abstract We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place for the other three. Our basic systems are built on Transformer, back translation and knowledge distillation. We integrate several of our rececent techniques to enhance the baseline systems: multi-agent dual learning (MADL), masked sequence-to-sequence pre-training (MASS), neural architecture optimization (NAO), and soft contextual data augmentation (SCA). 1 Introduction We participated in the WMT19 shared news translation task in 11 translation directions. We achieved first place for 8 directions: German English, German French, Chinese English, English Lithuanian, English Finnish, and Russian English, and three other directions were placed second (ranked by teams), which included Lithuanian English, Finnish English, and English Kazakh. Our basic systems are based on Transformer, back translation and knowledge distillation. We experimented with several techniques we proposed recently. In brief, the innovations we introduced are: Multi-agent dual learning (MADL) The core idea of dual learning is to leverage the duality between the primal task (mapping from domain X to domain Y) and dual task (mapping from domain Y to X) to boost the performances of both tasks. MADL (Wang et al., 2019) extends the dual learning (He et al., 2016; Xia et al., 2017a) framework by introducing multiple primal and dual models. It was integrated into our submitted systems for*Corresponding author.