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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.</abstract>
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%0 Conference Proceedings
%T Multi-layer Representation Fusion for Neural Machine Translation
%A Wang, Qiang
%A Li, Fuxue
%A Xiao, Tong
%A Li, Yanyang
%A Li, Yinqiao
%A Zhu, Jingbo
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F wang-etal-2018-multi-layer
%X 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.
%U https://aclanthology.org/C18-1255
%P 3015-3026

Markdown (Informal)

[Multi-layer Representation Fusion for Neural Machine Translation](https://aclanthology.org/C18-1255) (Wang et al., COLING 2018)

• Multi-layer Representation Fusion for Neural Machine Translation (Wang et al., COLING 2018)

ACL

• Qiang Wang, Fuxue Li, Tong Xiao, Yanyang Li, Yinqiao Li, and Jingbo Zhu. 2018. Multi-layer Representation Fusion for Neural Machine Translation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3015–3026, Santa Fe, New Mexico, USA. Association for Computational Linguistics.