An Efficient Transformer Decoder with Compressed Sub-layers

The large attention-based encoder-decoder network (Transformer) has become prevailing recently due to its effectiveness. But the high computation complexity of its decoder raises the inefficiency issue. By examining the mathematic formulation of the decoder, we show that under some mild conditions, the architecture could be simplified by compressing its sub-layers, the basic building block of Transformer, and achieves a higher parallelism. We thereby propose Compressed Attention Network, whose decoder layer consists of only one sub-layer instead of three. Extensive experiments on 14 WMT machine translation tasks show that our model is 1. 42× faster with performance on par with a strong baseline. This strong baseline is already 2× faster than the widely used standard baseline without loss in performance.

Authors

• Yanyang Li Independent Researcher
• Ye Lin
• Tong Xiao School of Computer Science and Engineering, Northeastern University, Shenyang, China NiuTrans Research, Shenyang, China
• Jingbo Zhu School of Computer Science and Engineering, Northeastern University, Shenyang, China NiuTrans Research, Shenyang, China

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