Hang Cao


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The NiuTrans Machine Translation Systems for WMT22
Weiqiao Shan | Zhiquan Cao | Yuchen Han | Siming Wu | Yimin Hu | Jie Wang | Yi Zhang | Hou Baoyu | Hang Cao | Chenghao Gao | Xiaowen Liu | Tong Xiao | Anxiang Ma | Jingbo Zhu
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper describes the NiuTrans neural machine translation systems of the WMT22 General MT constrained task. We participate in four directions, including Chinese→English, English→Croatian, and Livonian↔English. Our models are based on several advanced Transformer variants, e.g., Transformer-ODE, Universal Multiscale Transformer (UMST). The main workflow consists of data filtering, large-scale data augmentation (i.e., iterative back-translation, iterative knowledge distillation), and specific-domain fine-tuning. Moreover, we try several multi-domain methods, such as a multi-domain model structure and a multi-domain data clustering method, to rise to this year’s newly proposed multi-domain test set challenge. For low-resource scenarios, we build a multi-language translation model to enhance the performance, and try to use the pre-trained language model (mBERT) to initialize the translation model.


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The NiuTrans System for the WMT 2021 Efficiency Task
Chenglong Wang | Chi Hu | Yongyu Mu | Zhongxiang Yan | Siming Wu | Yimin Hu | Hang Cao | Bei Li | Ye Lin | Tong Xiao | Jingbo Zhu
Proceedings of the Sixth Conference on Machine Translation

This paper describes the NiuTrans system for the WMT21 translation efficiency task. Following last year’s work, we explore various techniques to improve the efficiency while maintaining translation quality. We investigate the combinations of lightweight Transformer architectures and knowledge distillation strategies. Also, we improve the translation efficiency with graph optimization, low precision, dynamic batching, and parallel pre/post-processing. Putting these together, our system can translate 247,000 words per second on an NVIDIA A100, being 3× faster than our last year’s system. Our system is the fastest and has the lowest memory consumption on the GPU-throughput track. The code, model, and pipeline will be available at NiuTrans.NMT.