Jianfei Feng


2022

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HW-TSC’s Submission for the WMT22 Efficiency Task
Hengchao Shang | Ting Hu | Daimeng Wei | Zongyao Li | Xianzhi Yu | Jianfei Feng | Ting Zhu | Lizhi Lei | Shimin Tao | Hao Yang | Ying Qin | Jinlong Yang | Zhiqiang Rao | Zhengzhe Yu
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2022 Efficiency Shared Task. For this year’s task, we still apply sentence-level distillation strategy to train small models with different configurations. Then, we integrate the average attention mechanism into the lightweight RNN model to pursue more efficient decoding. We tried adding a retrain step to our 8-bit and 4-bit models to achieve a balance between model size and quality. We still use Huawei Noah’s Bolt for INT8 inference and 4-bit storage. Coupled with Bolt’s support for batch inference and multi-core parallel computing, we finally submit models with different configurations to the CPU latency and throughput tracks to explore the Pareto frontiers.

2021

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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
Proceedings of the Sixth Conference on Machine Translation

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.