Q
Qiang Wang
Researcher at Northeastern University
Publications - 24
Citations - 739
Qiang Wang is an academic researcher from Northeastern University. The author has contributed to research in topics: Machine translation & Encoder. The author has an hindex of 7, co-authored 22 publications receiving 397 citations. Previous affiliations of Qiang Wang include Alibaba Group.
Papers
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Proceedings ArticleDOI
Learning Deep Transformer Models for Machine Translation.
TL;DR: This paper showed that a deep Transformer model can surpass the Transformer-Big counterpart by proper use of layer normalization and a novel way of passing the combination of previous layers to the next.
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Learning Deep Transformer Models for Machine Translation
TL;DR: It is claimed that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next.
Proceedings ArticleDOI
The NiuTrans Machine Translation Systems for WMT19.
Bei Li,Yinqiao Li,Chen Xu,Ye Lin,Jiqiang Liu,Hui Liu,Ziyang Wang,Yuhao Zhang,Nuo Xu,Zeyang Wang,Kai Feng,Hexuan Chen,Tengbo Liu,Yanyang Li,Qiang Wang,Tong Xiao,Jingbo Zhu +16 more
TL;DR: NuTrans neural machine translation systems for the WMT 2019 news translation tasks achieved the highest BLEU scores in {KK↔EN, GU→EN} directions, ranking 2nd in {RU→EN, DE↔CS} and 3rd in {ZH→en, LT→ EN, EN→RU, EN↔DE} among all constrained submissions.
Proceedings Article
Multi-layer Representation Fusion for Neural Machine Translation
TL;DR: This paper proposes a multi-layer representation fusion (MLRF) approach to fusing stacked layers and designs three fusion functions to learn a better representation from the stack in German-English translation.
Proceedings ArticleDOI
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation
TL;DR: This work offers a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT), which is robust to large beam sizes and not well studied in previous work.