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Unsupervised Parallel Sentences of Machine Translation for Asian Language Pairs

Shaolin Zhu,Chenggang Mi,Tianqi Li,Yong Yang,Chun Xu

Published 2022 in ACM Trans. Asian Low Resour. Lang. Inf. Process.

ABSTRACT

Parallel sentence pairs play a very important role in many natural language processing tasks, especially cross-lingual tasks such as machine translation. So far, many Asian language pairs lack bilingual parallel sentences. As collecting bilingual parallel data is very time-consuming and difficult, it is very important for many low-resource Asian language pairs. While existing methods have shown encouraging results, they rely on bilingual data seriously or have some drawbacks in an unsupervised situation. To address these issues, we propose a new unsupervised similarity calculation and dynamic selection metric to obtain parallel sentence pairs in an unsupervised situation. First, our method maps bilingual word embedding by postdoc adversarial training, which rotates the source space to match the target without parallel data. Then, we introduce a new cross-domain similarity adaption to obtain parallel sentence pairs. Experimental results on real-world datasets show that our model can obtain better accuracy and recall on mining parallel sentence pairs. We also show that the extracted bilingual sentence corpora can significantly improve the performance of neural machine translation.

PUBLICATION RECORD

  • Publication year

    2022

  • Venue

    ACM Trans. Asian Low Resour. Lang. Inf. Process.

  • Publication date

    2022-02-03

  • Fields of study

    Linguistics, Computer Science

  • Identifiers
    DOI 10.1145/3486677
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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