Hybrid machine translation (HMT) takes advantage of different types of machine translation
(MT) systems to improve translation performance. Neural machine translation (NMT) can
produce more fluent translations while phrase-based statistical machine translation (PB-SMT)
can produce adequate results primarily due to the contribution of the translation model. In
this paper, we propose a cascaded hybrid framework to combine NMT and PB-SMT to improve translation quality. Specifically, we first use the trained NMT system to pre-translate
the training data, and then employ the pre-translated training data to build an SMT system and
tune parameters using the pre-translated development set. Finally, the SMT system is utilised
as a post-processing step to re-decode the pre-translated test set and produce the final result.
Experiments conducted on Japanese!English and Chinese!English show that the proposed
cascaded hybrid framework can significantly improve performance by 2.38 BLEU points and
4.22 BLEU points, respectively, compared to the baseline NMT system.
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Funders:
ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106), SFI Industry Fellowship Programme 2016 (Grant 16/IFB/4490)
ID Code:
23357
Deposited On:
24 May 2019 15:12 by
Thomas Murtagh
. Last Modified 24 May 2019 15:12