Towards Compact and Fast Neural Machine Translation Using a Combined Method
文献类型：会议论文
| 作者 | Xiaowei Zhang1,2 ; Wei Chen1; Feng Wang1,2; Shuang Xu1; Bo Xu1
|
| 出版日期 | 2017-09 |
| 会议日期 | 2017-9 |
| 会议地点 | 丹麦哥本哈根 |
| 关键词 | Machine Translation Neural Network Model Compression Decoding Speedup |
| 页码 | 1475–1481 |
| 英文摘要 | Neural Machine Translation (NMT) lays intensive burden on computation and memory cost. It is a challenge to deploy NMT models on the devices with limited computation and memory budgets. This paper presents a four stage pipeline to compress model and speed up the decoding for NMT. Our method first introduces a compact architecture based on convolutional encoder and weight shared embeddings. Then weight pruning is applied to obtain a sparse model. Next, we propose a fast sequence interpolation approach which enables the greedy decoding to achieve performance on par with the beam search. Hence, the time-consuming beam search can be replaced by simple greedy decoding. Finally, vocabulary selection is used to reduce the computation of softmax layer. Our final model achieves 10× speedup, 17× parameters reduction, <35MB storage size and comparable performance compared to the baseline model. |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21185]  |
| 专题 | 类脑智能研究中心_神经计算及脑机交互 |
| 作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Xiaowei Zhang,Wei Chen,Feng Wang,et al. Towards Compact and Fast Neural Machine Translation Using a Combined Method[C]. 见:. 丹麦哥本哈根. 2017-9. |
入库方式： OAI收割
来源：自动化研究所
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