Conference Proceedings


Andy Way
Jinhua Du



pb smt hybrid machine translation english translation quality phrase based statistical machine translation machine translating training data

Neural pre-translation for hybrid machine translation (2017)

Abstract 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.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Andy Way, Jinhua Du

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Andy Way
Dublin City University
Total Publications: 229
Jinhua Du
Dublin City University
Total Publications: 38