HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task
Hengchao Shang, Ting Hu, Daimeng Wei, Zongyao Li, Jianfei Feng, Zhengzhe Yu, Jiaxin Guo, Shaojun Li, Lizhi Lei, Shimin Tao, Hao Yang, Jun Yao, Ying Qin
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This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task. We explore the sentence-level teacher-student distillation technique and train several small-size models that find a balance between efficiency and quality. Our models feature deep encoder, shallow decoder and light-weight RNN with SSRU layer. We use Huawei Noah’s Bolt, an efficient and light-weight library for on-device inference. Leveraging INT8 quantization, self-defined General Matrix Multiplication (GEMM) operator, shortlist, greedy search and caching, we submit four small-size and efficient translation models with high translation quality for the one CPU core latency track.