Abstract

Translation memories (TM) facilitate human translators to reuse existing repetitive translation fragments. In this paper, we propose a novel method to combine the strengths of both TM and neural machine translation (NMT) for high-quality translation. We treat the target translation of a TM match as an additional reference input and encode it into NMT with an extra encoder. A gating mechanism is further used to balance the impact of the TM match on the NMT decoder. Experiment results on the UN corpus demonstrate that when fuzzy matches are higher than 50%, the quality of NMT translation can be significantly improved by over 10 BLEU points.

Highlights

  • Neural machine translation, an emerging machine translation (MT) technology, has made remarkable progress in the past few years (Cho et al, 2014; Sutskever et al, 2014), which strongly encourages many translation agencies to embrace it for product deployment

  • A series of experiments on the Chinese-English UN corpus demonstrate that when fuzzy matches are over 50%, the proposed method can significantly improve neural machine translation (NMT) with the gated Translation memories (TM) signal

  • We can find that when fuzzy match scores are over 50%, the extra introduction of TM information can significantly help NMT to better translate

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Summary

Introduction

An emerging machine translation (MT) technology, has made remarkable progress in the past few years (Cho et al, 2014; Sutskever et al, 2014), which strongly encourages many translation agencies to embrace it for product deployment. A natural question during this deployment is how the strengths of both the traditional TM and new NMT technologies can be combined together for professional high-quality translation. Such attempts to the TM and MT combination have been already conducted in the context of statistical machine translation (SMT). A variety of efforts have been made to incorporate matched translation segments from TM into SMT (Koehn and Senellart, 2010). Inspired by these efforts, we aim at combining TM and NMT in this paper.

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