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Neural Machine Translation for Financial Listing Documents

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11305))

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Abstract

In this paper, we focus on developing a Neural Machine Translation (NMT) system on English-to-Traditional-Chinese translation for financial prospectuses of companies which seek listing on the Hong Kong Stock Exchange. To the best of our knowledge, this is the first work on NMT for this specific domain. We propose a domain-specific NMT system by introducing a domain flag to indicate the target-side domain. By training the NMT model on the data from both the IPO corpus and the general domain corpus, we can expand the vocabulary while capturing the common writing styles and sentence structures. Our experimental results show that the proposed NMT system can achieve a significant improvement on translating the IPO documents. More significantly, through a blind assessment by a translator expert, our system outperforms two mainstream commercial tools, the Google translator and SDL Trado for some IPO documents.

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Notes

  1. 1.

    https://www.hkex.com.hk/.

  2. 2.

    https://github.com/BYVoid/OpenCC.

  3. 3.

    https://github.com/fxsjy/jieba.

  4. 4.

    https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl.

  5. 5.

    https://github.com/google/sentencepiece.

  6. 6.

    https://github.com/tensorflow/tensor2tensor.

  7. 7.

    https://translate.google.com.

  8. 8.

    https://www.freetranslation.com.

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Acknowledgments

The work described in this paper was partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/IDS14/16).

Author information

Authors and Affiliations

  1. Deep Learning Research and Application Centre, Hang Seng Management College, Siu Lek Yuen, Hong Kong

    Linkai Luo, Haiqin Yang, Sai Cheong Siu & Francis Yuk Lun Chin

  2. MTdata, Meitu, Xiamen, China

    Haiqin Yang

  3. Department of Computer Science, The University of Hong Kong, Lung Fu Shan, Hong Kong

    Francis Yuk Lun Chin

Authors
  1. Linkai Luo
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  2. Haiqin Yang
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  3. Sai Cheong Siu
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  4. Francis Yuk Lun Chin
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Corresponding author

Correspondence to Linkai Luo .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Luo, L., Yang, H., Siu, S.C., Chin, F.Y.L. (2018). Neural Machine Translation for Financial Listing Documents. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_21

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  • DOI: https://doi.org/10.1007/978-3-030-04221-9_21

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