  Neural Machine Translation (NMT) has achieved notable success in recent
years. Such a framework usually generates translations in isolation. In
contrast, human translators often refer to reference data, either rephrasing
the intricate sentence fragments with common terms in source language, or just
accessing to the golden translation directly. In this paper, we propose a
Reference Network to incorporate referring process into translation decoding of
NMT. To construct a \emph{reference book}, an intuitive way is to store the
detailed translation history with extra memory, which is computationally
expensive. Instead, we employ Local Coordinates Coding (LCC) to obtain global
context vectors containing monolingual and bilingual contextual information for
NMT decoding. Experimental results on Chinese-English and English-German tasks
demonstrate that our proposed model is effective in improving the translation
quality with lightweight computation cost.
