activity
20192023
most citedLearning Deep Transformer Models for Machine Translation

97 citations · 148 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2023

Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning

Rongxiang Weng, Qiang Wang, Wensen Cheng +2

Neural machine translation (NMT) has achieved remarkable success in producing high-quality translations. However, current NMT systems suffer from a lack of reliability, as their ou…

cs.CL2020

Layer-Wise Multi-View Learning for Neural Machine Translation

Qiang Wang, Changliang Li, Yue Zhang +2

Traditional neural machine translation is limited to the topmost encoder layer's context representation and cannot directly perceive the lower encoder layers. Existing solutions us…

cs.CL2020★ 1 cited

Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation

Qiang Wang, Tong Xiao, Jingbo Zhu

The standard neural machine translation model can only decode with the same depth configuration as training. Restricted by this feature, we have to deploy models of various sizes t…

cs.CL2020★ 4 cited

Neural Machine Translation with Joint Representation

Yanyang Li, Qiang Wang, Tong Xiao +2

Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units,…

cs.CL2020★ 46 cited

Multi-layer Representation Fusion for Neural Machine Translation

Qiang Wang, Fuxue Li, Tong Xiao +3

Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no acces…

cs.CL2019★ 97 cited

Learning Deep Transformer Models for Machine Translation

Qiang Wang, Bei Li, Tong Xiao +4

Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide netwo…