Sunzhu Li
2022
Hypoformer: Hybrid Decomposition Transformer for Edge-friendly Neural Machine Translation
Sunzhu Li
|
Peng Zhang
|
Guobing Gan
|
Xiuqing Lv
|
Benyou Wang
|
Junqiu Wei
|
Xin Jiang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Transformer has been demonstrated effective in Neural Machine Translation (NMT). However, it is memory-consuming and time-consuming in edge devices, resulting in some difficulties for real-time feedback. To compress and accelerate Transformer, we propose a Hybrid Tensor-Train (HTT) decomposition, which retains full rank and meanwhile reduces operations and parameters. A Transformer using HTT, named Hypoformer, consistently and notably outperforms the recent light-weight SOTA methods on three standard translation tasks under different parameter and speed scales. In extreme low resource scenarios, Hypoformer has 7.1 points absolute improvement in BLEU and 1.27 X speedup than vanilla Transformer on IWSLT’14 De-En task.
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Co-authors
- Peng Zhang 1
- Guobing Gan 1
- Xiuqing Lv 1
- Benyou Wang 1
- Junqiu Wei 1
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