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arXiv · 2002.06714

Multi-layer Representation Fusion for Neural Machine Translation

Qiang Wang·Fuxue Li·Tong Xiao·Yanyang Li·Yinqiao Li·Jingbo Zhu
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Abstract

Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation.

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Qiang Wang, Fuxue Li, Tong Xiao, Yanyang Li, Yinqiao Li, Jingbo Zhu. 2020-02-16. Multi-layer Representation Fusion for Neural Machine Translation. https://arxiv.org/abs/2002.06714

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