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A Chunk-Based Multi-strategy Machine Translation Method

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Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 891))

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Abstract

In this paper, a chunk-based multi-strategy machine translation method is proposed. Firstly, an English-Chinese bilingual tree-bank is constructed. Then, a translation strategy based on the chunk that combines statistics and rules is used in the translation stage. Through hierarchical sub-chunks, the input sentence is divided into a set of chunk sequence. Each chunk searches the corresponding instance in the corpus. Translation is completed by recursive refinement from chunks to words. Conditional Random Fields model is used to divide chunks. An experimental English-Chinese translation system is deployed, and experimental results show that the system performs better than the Systran system.

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References

  1. Babhulgaonkar, A.R., Bharad, S.V.: Statistical machine translation. In: 1st International Conference on Intelligent Systems and Information Management, pp. 62–67. Institute of Electrical and Electronics Engineers Inc. (2017)

    Google Scholar 

  2. Gong, H.: The role of speech recognition and machine translation in interpreting. Study Lang. Arts Sports 5, 383–385 (2018)

    Google Scholar 

  3. Semmar, N., Laib, M.: Building multiword expressions bilingual lexicons for domain adaptation of an example-based machine translation system. In: 11th International Conference on Recent Advances in Natural Language Processing, pp. 661–669. Association for Computational Linguistics (2017)

    Google Scholar 

  4. Chua, C.C., Lim, T.Y., Soon, L.: Meaning preservation in example-based machine translation with structural semantics. Expert Syst. Appl. 78, 242–258 (2017)

    Article  Google Scholar 

  5. Mahata, S.K., Das, D., Bandyopadhyay, S.: MTIL2017: machine translation using recurrent neural network on statistical machine translation. J. Intell. Syst. (2018)

    Google Scholar 

  6. Wang, X., Lu, Z., Tu, Z., et al.: Neural machine translation advised by statistical machine translation. In: 31st AAAI Conference on Artificial Intelligence, pp. 3330–3336. AAAI press (2017)

    Google Scholar 

  7. Sun, L., Jin, Y., Du, L., Sun, Y.: Automatic extraction of bilingual term lexicon from parallel corpora. J. Chin. Inform. Process. 14(6), 33–39 (2000)

    Google Scholar 

  8. Branco, A., Carvalheiro, C., Costa, F., et al.: DeepBankPT and companion Portuguese treebanks in a multilingual collection of treebanks aligned with the penn Treebank. In: 11th International Conference on Computational Processing of Portuguese, pp. 207–213. Springer (2014)

    Google Scholar 

  9. Badmaeva, E., Tyers, F.M.: A dependency treebank for Buryat. In: 17th International Conference on Intelligent Text Processing and Computational Linguistics, pp. 397–408. Springer (2018)

    Google Scholar 

  10. Bielinskiene, A., Boizou, L., Kovalevskaite, J., Rimkute, E.: Lithuanian dependency treebank ALKSNIS. In: 7th International Conference on Human Language Technologies - The Baltic Perspective, pp. 107–114. IOS Press (2016)

    Google Scholar 

  11. Song, D., Liu, W., Zhou, T., et al.: Efficient robust conditional random fields. IEEE Trans. Image Process. 24(10), 3124–3136 (2015)

    Article  MathSciNet  Google Scholar 

  12. BLEU-WIKIPEDIA. https://en.wikipedia.org/wiki/BLEU. Accessed 12 Feb 2018

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Acknowledgment

The authors are very grateful to Special Projects for Reform and Development of Beijing Institute of Science and Technology Information (2018) (Information rapid processing capacity building with applied artificial intelligence and big data technology) for the supports and assistance.

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Authors and Affiliations

  1. Beijing Institute of Science and Technology Information, Beijing, 100044, People’s Republic of China

    Yiou Wang

  2. Digital Performance and Simulation Technology Lab, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China

    Fuquan Zhang

  3. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, People’s Republic of China

    Fuquan Zhang

Authors
  1. Yiou Wang
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  2. Fuquan Zhang
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Corresponding author

Correspondence to Fuquan Zhang .

Editor information

Editors and Affiliations

  1. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Pavel Krömer

  2. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, China

    Hong Zhang

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

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Wang, Y., Zhang, F. (2019). A Chunk-Based Multi-strategy Machine Translation Method. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_38

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