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Improving Neural Machine Translation by Retrieving Target Translation Template

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14089))

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Abstract

In the neural machine translation (NMT) paradigm, transformer-based NMT has achieved great progress in recent years. It is based on the standard end-to-end structure, and acquires translation knowledge through the attention mechanism from the parallel corpus automatically without human intervention. Inspired by the process of translating sentences by human translators and the successful application of translation template in statistical machine translation, this paper proposes a novel approach to incorporate the target translation template into the Transformer-based NMT model. Firstly, the template extraction method derives the parallel templates corpus from the constituency parse tree. Secondly, given a sentence to be translated, a fuzzy matching strategy is proposed to calculate the most possible target translation template from the parallel template corpus. Finally, an effective method is proposed to incorporate the target translate template into the Transformer-based NMT model. Experimental results on three translation tasks demonstrate the effectiveness of the proposed approach and it improves the translation quality significantly.

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Acknowledgement

This work was supported by National Natural Science Foundation of Liaoning Province, China (Grant no. 2021-YKLH-12, 2022-YKLH-18), Scientific Research Foundation of Liaoning Province (Grant no. LJKQZ2021184), High-level talents research project of Yingkou Institute of Technology (Grant No. YJRC202026).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Fuxue Li

  2. College of Electrical Engineering, Yingkou Institute of Technology, Yingkou, China

    Fuxue Li, Hong Yan & Zhen Zhang

  3. College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, China

    Chuncheng Chi

Authors
  1. Fuxue Li
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  2. Chuncheng Chi
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  3. Hong Yan
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  4. Zhen Zhang
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Corresponding author

Correspondence to Fuxue Li .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, F., Chi, C., Yan, H., Zhang, Z. (2023). Improving Neural Machine Translation by Retrieving Target Translation Template. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_54

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  • DOI: https://doi.org/10.1007/978-981-99-4752-2_54

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4751-5

  • Online ISBN: 978-981-99-4752-2

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