6 citations · 13 across the 5 of their papers we have counts for
7 papers
Secoco: Self-Correcting Encoding for Neural Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang +3
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting pred…
The Volctrans Neural Speech Translation System for IWSLT 2021
Chengqi Zhao, Zhicheng Liu, Jian Tong +6
This paper describes the systems submitted to IWSLT 2021 by the Volctrans team. We participate in the offline speech translation and text-to-text simultaneous translation tracks. F…
Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang +2
Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, the currently existing neural machine translation delivers unsatisfying result…
Counter-Interference Adapter for Multilingual Machine Translation
Yaoming Zhu, Jiangtao Feng, Chengqi Zhao +2
Developing a unified multilingual model has long been a pursuit for machine translation. However, existing approaches suffer from performance degradation -- a single multilingual m…
Finding Sparse Structures for Domain Specific Neural Machine Translation
Jianze Liang, Chengqi Zhao, Mingxuan Wang +2
Neural machine translation often adopts the fine-tuning approach to adapt to specific domains. However, nonrestricted fine-tuning can easily degrade on the general domain and over-…
NeurST: Neural Speech Translation Toolkit
Chengqi Zhao, Mingxuan Wang, Qianqian Dong +2
NeurST is an open-source toolkit for neural speech translation. The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced sp…