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Chinese–Portuguese machine translation: a study on building parallel corpora from comparable texts

Liu, Siyou, Wang, Longyue orcid logoORCID: 0000-0002-9062-6183 and Liu, Chao-Hong orcid logoORCID: 0000-0002-1235-6026 (2018) Chinese–Portuguese machine translation: a study on building parallel corpora from comparable texts. In: LREC 2018 - 11th International Conference on Language Resources and Evaluation, 7-12 May 2018, Miyazaki, Japan. ISBN 979-10-95546-19-1

Although there are increasing and significant ties between China and Portuguese-speaking countries, there is not much parallel corpora in the Chinese–Portuguese language pair. Both languages are very populous, with 1.2 billion native Chinese speakers and 279 million native Portuguese speakers, the language pair, however, could be considered as low-resource in terms of available parallel corpora. In this paper, we describe our methods to curate Chinese–Portuguese parallel corpora and evaluate their quality. We extracted bilingual data from Macao government websites and proposed a hierarchical strategy to build a large parallel corpus. Experiments are conducted on existing and our corpora using both Phrased-Based Machine Translation (PBMT) and the state-of-the-art Neural Machine Translation (NMT) models. The results of this work can be used as a benchmark for future Chinese–Portuguese MT systems. The approach we used in this paper also show a good example on how to boost performance of MT systems for low-resource language pairs.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Chinese–Portuguese; Low-Resource; Statistical Machine Translation; Neural Machine Translation; Parallel Corpus
Subjects:Computer Science > Machine learning
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 11th edition of the Language Resources and Evaluation Conference. . European Language Resource Association. ISBN 979-10-95546-19-1
Publisher:European Language Resource Association
Copyright Information:© 2018 ELRA
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant No. 13/RC/2106) and is co-funded under the European Regional Development Fund, European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska-Curie Actions (Grant No. 734211).
ID Code:23205
Deposited On:24 Apr 2019 15:38 by Thomas Murtagh . Last Modified 24 Apr 2019 15:38

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