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CASICT-DCU neural machine translation systems for WMT17

Zhang, Jinchao, Porkaew, Peerachet, Hu, Jiawei, Zhao, Qiuye and Liu, Qun orcid logoORCID: 0000-0002-7000-1792 (2017) CASICT-DCU neural machine translation systems for WMT17. In: Second Conference on Machine Translation (WMT17), 7-8 Sept 2017, Copenhagen, Denmark.

Abstract
We participated in the WMT 2016 shared news translation task on English ↔ Chinese language pair. Our systems are based on the encoder-decoder neural machine translation model with the attention mechanism. We employ the Gated Recurrent Unit (GRU) with the linear associative connection to build deep encoder and address the unknown words with the dictionary replace approach. The dictionaries are extracted from the parallel training data with unsupervised word alignment method. In the decoding procedure, the translation probabilities of the target word from different models are averagely combined as the ensemble strategy. In this paper, we introduce our systems from data preprocessing to post-editing in details.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Proceedings of the 2nd Conference on Machine Translation (WMT17), Volume 2: Shared Task Papers. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://dx.doi.org/10.18653/v1/W17-4745
Copyright Information:© 2017 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland in the ADAPT Centre for Digital Content Technology (www.adaptcentre.ie) at Dublin City University funded under the SFI Research Centres Programme (Grant 13/RC/2106) co-funded under the European Regional Development Fund.
ID Code:23331
Deposited On:21 May 2019 08:28 by Thomas Murtagh . Last Modified 24 Jul 2019 14:20
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