Portrait of Xu Tan

Xu Tan

Associate Researcher 2

About

Xu Tan is currently an Associate Researcher in Machine Learning Group, Microsoft Research Asia (MSRA). He graduated from Zhejiang University on March, 2015. His research interests mainly lie in machine learning, deep learning, and their applications on natural language processing and speech processing, including neural machine translation, text to speech, etc. He also has interests in distributed machine learning, search ranking and recommendation. 

Recent Research

Projects

Publications

  • Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu, FastSpeech: Fast, Robust and Controllable Text to Speech. arXiv 2019. [Paper] [Demo][Article] [Reddit]
  • Yichong Leng, Xu Tan, Tao Qin, Xiang-Yang Li and Tie-Yan Liu, Unsupervised Pivot Translation for Distant Languages, ACL 2019. [Paper]
  • Tianyu He, Yingce Xia, Jianxin Lin, Xu Tan, Di He, Tao Qin, Zhibo Chen, Deliberation Learning for Image-to-Image Translation, IJCAI 2019.
  • Hao Sun, Xu Tan, Jun-Wei Gan, Hongzhi Liu, Sheng Zhao, Tao Qin, Tie-Yan Liu, Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion. INTERSPEECH 2019. [Paper]
  • Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Sheng Zhao, Tie-Yan Liu, Almost Unsupervised Text to Speech and Automatic Speech Recognition, ICML 2019. [Paper] [Demo] [Article] [Blog] [Slides] [Video]
  • Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, MASS: Masked Sequence to Sequence Pre-training for Language Generation, ICML 2019. [Paper][Code@Github][Article][Blog]
  • Xu Tan, Yi Ren, Di He, Tao Qin, Tie-Yan Liu, Multilingual Neural Machine Translation with Knowledge Distillation, ICLR 2019. [Paper] [Code@GitHub]
  • Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu, Representation Degeneration Problem in Training Natural Language Generation Models, ICLR 2019. [Paper]
  • Junliang Guo, Xu Tan, Di He, Tao Qin, and Tie-Yan Liu, Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input, AAAI 2019. [Paper]
  • Chengyue Gong, Xu Tan, Di He, and Tao Qin, Sentence-wise Smooth Regularization for Sequence to Sequence Learning, AAAI 2019. [Paper]
  • Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, and Tao Qin, Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder, AAAI 2019. [Paper]
  • Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tie-Yan Liu, FRAGE: Frequency-Agnostic Word Representation, NIPS 2018. [Paper] [Code@Github]
  • Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, and Tie-Yan Liu, Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation, NIPS 2018.[Paper]
  • Xu Tan, Lijun Wu, Di He, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu, Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter, EMNLP 2018. [Paper]
  • Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Model-Level Dual Learning, ICML 2018. [Paper]
  • Kaitao Song, Xu Tan, Furong Peng, Jianfeng Lu, Hybrid Self-Attention Network for Machine Translation, arXiv 2018. [Paper]
  • Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, and Tie-Yan Liu, Double Path Networks for Sequence to Sequence Learning, COLING 2018. [Paper] [Code@Github]
  • Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, Ming Zhou, Achieving Human Parity on Automatic Chinese to English News Translation, arXiv 2018. [Paper] [Article-1] [Article-2] [Video]
  • Yanyao Shen, Xu Tan, Di He, Tao Qin, and Tie-Yan Liu, Dense Information Flow for Neural Machine Translation, NAACL 2018. [Paper] [Code@Github]
  • Xu Tan, Yuanchao Shu, Xie Lu, Peng Cheng, Jiming Chen, Characterizing and Modeling Package Dynamics in Express Shipping Service Network, IEEE Big Data 2014. [Paper]

Publications

English
Français du Canada English