Abstract
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 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.