IEICE TRANSACTIONS on Fundamentals
Research on Mongolian-Chinese Translation Model Based on Transformer with Soft Context Data Augmentation Technique
Summary :
As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.5 pp.871-876
- Publication Date
- 2022/05/01
- Publicized
- 2021/11/19
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021EAP1121
- Type of Manuscript
- PAPER
- Category
- Neural Networks and Bioengineering
Authors
Qing-dao-er-ji REN
Inner Mongolia University of Technology
Yuan LI
Inner Mongolia University of Technology
Shi BAO
Inner Mongolia University of Technology
Yong-chao LIU
Inner Mongolia University of Technology
Xiu-hong CHEN
Hohhot, Inner Mongolia
Keyword
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Qing-dao-er-ji REN, Yuan LI, Shi BAO, Yong-chao LIU, Xiu-hong CHEN, "Research on Mongolian-Chinese Translation Model Based on Transformer with Soft Context Data Augmentation Technique" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 5, pp. 871-876, May 2022, doi: 10.1587/transfun.2021EAP1121.
Abstract: As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAP1121/_p
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@ARTICLE{e105-a_5_871,
author={Qing-dao-er-ji REN, Yuan LI, Shi BAO, Yong-chao LIU, Xiu-hong CHEN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Research on Mongolian-Chinese Translation Model Based on Transformer with Soft Context Data Augmentation Technique},
year={2022},
volume={E105-A},
number={5},
pages={871-876},
abstract={As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.},
keywords={},
doi={10.1587/transfun.2021EAP1121},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Research on Mongolian-Chinese Translation Model Based on Transformer with Soft Context Data Augmentation Technique
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 871
EP - 876
AU - Qing-dao-er-ji REN
AU - Yuan LI
AU - Shi BAO
AU - Yong-chao LIU
AU - Xiu-hong CHEN
PY - 2022
DO - 10.1587/transfun.2021EAP1121
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2022
AB - As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.
ER -
English
