Top > JACIII > Template-Based Model for Mongolian-Chinese Machine Translation

single-jc.php

JACIII Vol.20 No.6 pp. 893-901
doi: 10.20965/jaciii.2016.p0893
(2016)

Paper:

Views over last 60 days: 848

Template-Based Model for Mongolian-Chinese Machine Translation

Jing Wu, Hongxu Hou, Feilong Bao, and Yupeng Jiang

College of Computer Science, Inner Mongolia University
Hohhot, China

Received:
February 21, 2016
Accepted:
July 13, 2016
Published:
November 20, 2016
Creative Commons License
Keywords:
machine translation, template extraction, template translation, combined system, TBMT
Abstract
Mongolian and Chinese statistical machine translation (SMT) system has its limitation because of the complex Mongolian morphology, scarce resource of parallel corpus and the significant syntax differences. To address these problems, we propose a template-based machine translation (TBMT) system and combine it with the SMT system to achieve a better translation performance. The TBMT model we proposed includes a template extraction model and a template translation model. In the template extraction model, we present a novel method of aligning and abstracting static words from bilingual parallel corpus to extract templates automatically. In the template translation model, our specially designed method of filtering out the low quality matches can enhance the translation performance. Moreover, we apply lemmatization and Latinization to address data sparsity and do the fuzzy match. Experimentally, the coverage of TBMT system is over 50%. The combined SMT system translates all the other uncovered source sentences. The TBMT system outperforms the baselines of phrase-based and hierarchical phrase-based SMT systems for +3.08 and +1.40 BLEU points. The combined system of TBMT and SMT systems also performs better than the baselines of +2.49 and +0.81 BLEU points.
Cite this article as:
J. Wu, H. Hou, F. Bao, and Y. Jiang, “Template-Based Model for Mongolian-Chinese Machine Translation,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.6, pp. 893-901, 2016.
Data files:
References
  1. [1] J. Wu, H. X. Hou, Monghjaya, F. L. Bao, and C. J. Xie, “Introduction of Traditional Mongolian-Chinese Machine Translation,” Int. Conf. on Electrical, Automation and Mechanical Engineering (EAME), Phuket, pp. 357-360, 2015.
  2. [2] L. Qun, “Recent Developments in Machine Translation Research,” Contemporary Linguistics, Vol.11, No.2, pp. 21-26, 2009.
  3. [3] D. D. Ahn, S. F. Adafre, and M. De Rijke, “Towards Task-Based Temporal Extraction and Recognition,” Proc. on Annotating, Extracting, and Reasoning about Timeand Events, Schloss Dagstuhl, Germany, pp. 193-205, 2005.
  4. [4] R. D. Brown, “Example-Based Machine Translation in the Pangloss System,” Proc. of the 16th Int. Conf. on Computational Linguistics, Cpenhagen, pp. 169-174, 1996.
  5. [5] H. Altay Güvenir and Ilyas Cicekli, “Learning Translation Templates from Examples,” Information System, Vol.23, No.6, pp. 353-363, 1998.
  6. [6] H. Kaji and Y. Kida et al., “Learning Translation Templates from Bilingual Text,” Proc. of the 15th Int. Conf. on Computation Linguistics, Nantes, pp. 672-678, 1992.
  7. [7] H. Watanabe, S. Kurohashi, and E. Aramaki, “Finding Structural Correspondences from Bilingual Parsed Corpus for Corpus2based Translation,” Proc. of the 18th Int. Conf. on Computational Linguistics, pp. 906-912, 2000.
  8. [8] E. Yang, X. Lv and J. Zhu et al., “A Method of Chinese-English Translation Template Extraction” Proc. of the 13th China National Conf. on Computational Linguistics, Harbin, China, pp. 431-436, 2003.
  9. [9] F. J. Och and H. Ney, “A systematic comparison of various statistical alignment models,” Computational Linguistics, Vol.29, No.1, pp. 19-51, 2003.
  10. [10] M. Yu and H. Hou, “Researching of Mongolian Word Segmentation System Based on Dictionary, Rules and Language Model,” Inner Mongolian University. 2011.
  11. [11] K. Papineni, S. Roukos, T. Ward et al., “BLEU: A Method for Automatic Evaluation of Machine Translation Evaluation,” Proc. of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 201-228, 2002.
  12. [12] P. Koehn, F. J. Och, and D. Marcu, “Statistical Phrase-based Translation,” Proc. of NAACL, 2003.
  13. [13] D. Chiang, “Hierarchical Phrase–Based Translation,” Int. Conf. on Computational Linguistics, Vol.33, No.2: pp. 201-228, 2007.
  14. [14] R. Zhang, K. Yasuda, and E. Sumita, “Improved statistical machine translation by multiple Chinese word segmentation,” Proc. of the 3rd Workshop on Statistical Machine Trans., pp. 216-223, 2008.
  15. [15] H. Zhao, Y. Lv, G. Ben, Y. Huang, and Q. Liu, “Summary on CWMT2011 MT Trans-lation Evaluation,” J. of Chinese Information Processing, Vol.26, No.1: pp. 22-30, 2012.
  16. [16] P. Koehn, H. Hoang, A. Birch, et al., “Moses: Open Source Toolkit for Statistical Machine Translation,” Proc. of the Association for Computational Linguistics, pp. 177-180, 2007.
  17. [17] F. J. Och, “Minimum Error Rate Training in Statistical Machine Translation,” Proc. of the Association for Computational Linguistics, pp. 440-447, 2003.
  18. [18] C. Su, and H. Hou, “Hierarchical Phrase-based Mongolian-Chinese Statistical Machine translation,” Inner Mongolian University, 2014.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024