The NiuTrans Machine Translation System for WMT18

By Qiang Wang, Bei Li, Jiqiang Liu, B. Jiang, Zheyang Zhang, Yinqiao Li, Ye Lin, Tong Xiao, Jingbo Zhu. 2018

13 citations recorded by the source corpus.

This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks. Our baseline systems are based on the Transformer architecture. We further improve the translation performance 2.4-2.6 BLEU points from four aspects, including architectural improvements, diverse ensemble decoding, reranking, and post-processing. Among constrained submissions, we rank 2nd out of 16 submitted systems on Chinese → English task and 3rd out of 16 on English → Chinese task, respectively.

Subfields: Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Tong Xiao
2. Jingbo Zhu
3. Bei Li
4. Qiang Wang
5. Yinqiao Li
6. Ziyang Wang
7. Derek F. Wong
8. Lidia S. Chao

Citations in the corpus

• The NiuTrans Machine Translation Systems for WMT19 (2019, methodology): “It has been proved effective in the past few years’ WMT tasks (Wang et al., 2018; Deng et al., 2018; Junczys-Dowmunt, 2018; Sennrich et al., 2016a).”
• Learning Deep Transformer Models for Machine Translation (2019, methodology): “We refer the reader to Wang et al. (2018a) for the details.”