Reference Network for Neural Machine Translation

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 \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.

arXiv comment11 pages, 3 figures, accepted by ACL-2019

References in corpus (6)

• Sequence to Sequence Learning with Neural Networks
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Convolutional Sequence to Sequence Learning
• Does Neural Machine Translation Benefit from Larger Context?
• Adversarial Learning with Local Coordinate Coding