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20152019
most citedLearning What Data to Learn

54 citations · 73 across the 5 of their papers we have counts for

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Showing cs.CLShow all

7 papers · 1 filter

cs.CL2019

Microsoft Research Asia's Systems for WMT19

Yingce Xia, Xu Tan, Fei Tian +11

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 fo…

cs.CL2019

Hint-Based Training for Non-Autoregressive Machine Translation

Zhuohan Li, Zi Lin, Di He +4

Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer…

cs.CL20191 cited

Depth Growing for Neural Machine Translation

Lijun Wu, Yiren Wang, Yingce Xia +5

While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of neural machine translation (…

cs.CL201918 cited

Non-Autoregressive Machine Translation with Auxiliary Regularization

Yiren Wang, Fei Tian, Di He +3

As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the hi…

cs.CL2018

Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter

Lijun Wu, Xu Tan, Di He +4

Neural machine translation usually adopts autoregressive models and suffers from exposure bias as well as the consequent error propagation problem. Many previous works have discuss…

cs.CL2018

Achieving Human Parity on Automatic Chinese to English News Translation

Hany Hassan, Anthony Aue, Chang Chen +21

Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate acr…