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Chinese-Naxi machine translation method based on Naxi dependency language model

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Abstract

To make use of the syntactic characteristic of Naxi language effectively, in this paper, a Chinese-Naxi machine translation method based on Naxi dependency language model is proposed. The method, firstly, makes dependency parsing on the Chinese sentence, and extracts Chinese-Naxi dependency tree-to-string translation templates. Secondly, it decodes Naxi Sentence by using the templates, and generates n-best candidate sentences. Thirdly, it makes dependency parsing on each of the candidate Naxi sentences, and obtains a node sequence corresponding to the dependency relationships. Finally, it builds a Naxi dependency language model, with the help of the model, calculates and reorders each node from the sequence, and selects the final target sentence. The comparative experiments, on the proposed method, the existing tree-to-string translating method, Bruin and Mo-tse, show that the Chinese-Naxi machine translation method combined with the Naxi dependency language model can improve the translating accuracy effectively, and its BLEU-2 increases by 2 point than without the Naxi dependency language model.

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Acknowledgments

This work was supported by National Natural Science Foundation of China, Contract No. 61163022.

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Authors and Affiliations

  1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

    Shengxiang Gao, Xiuzhen Yang, Zhengtao Yu, Xiao Pan & Jianyi Guo

  2. Key Laboratory of Intelligent Information Processing, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

    Shengxiang Gao, Xiuzhen Yang, Zhengtao Yu, Xiao Pan & Jianyi Guo

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  1. Shengxiang Gao
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  2. Xiuzhen Yang
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  3. Zhengtao Yu
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  4. Xiao Pan
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  5. Jianyi Guo
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Correspondence to Zhengtao Yu.

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Gao, S., Yang, X., Yu, Z. et al. Chinese-Naxi machine translation method based on Naxi dependency language model. Int. J. Mach. Learn. & Cyber. 8, 333–342 (2017). https://doi.org/10.1007/s13042-014-0325-2

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  • DOI: https://doi.org/10.1007/s13042-014-0325-2

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