Chinese–Portuguese machine translation:
a study on building parallel corpora from comparable texts
Liu, Siyou, Wang, LongyueORCID: 0000-0002-9062-6183 and Liu, Chao-HongORCID: 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
Refereed:
Yes
Uncontrolled Keywords:
Chinese–Portuguese; Low-Resource; Statistical Machine Translation; Neural Machine Translation; Parallel Corpus
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