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Description and Findings of OPPO’s Machine Translation Systems for CCMT 2020

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Machine Translation (CCMT 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1328))

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Abstract

This paper demonstrates our machine translation systems for the CCMT 2020, which is composed of four parts. The last three parts report our results in the contest, each respectively focuses on English-Chinese bi-direction translation, Japanese-Chinese-English multi-lingual translation (patent domain), and Chinese minority languages to Mandarin Chinese translation. In each part, we will demonstrate our work on data pre-processing, model training as well as the application of general techniques, such as back-translation, ensemble and reranking. Besides, during our experiments, we surprisingly found that simply applying different Chinese word segmentation tools on low-resource corpora could bring obvious benefit across different tasks, and we will separate an independent section to discuss this finding. Among the 7 directions we participated in, we ranked the first in 6 tasks (For the corpus filtering task, we ranked first in the 500 million words sub-task) and the second for the rest.

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Notes

  1. 1.

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

  2. 2.

    http://www.xunsearch.com/scws/.

  3. 3.

    Including datasets released by WMT 2020, which are allowed in CCMT 2020.

  4. 4.

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

  5. 5.

    https://github.com/clab/fast_align.

  6. 6.

    https://taku910.github.io/mecab/.

  7. 7.

    https://github.com/mjpost/sacrebleu.

  8. 8.

    More accurately, morpheme-based model.

  9. 9.

    For Uighur \(\rightarrow \) Mandarin task we didn’t filter the corpus according to alignment information, since we find sometimes a Mandarin word can be a long phrase in Uighur. e.g. “ ” (rule of law) is officially translated to “qanun arqiliq idare qilish”. (Uighur here is transliterated by Uighur Latin alphabet (ULY)). For Tibetan, alignment information is calculated on a character-level corpus, means not only the Tibetan data is segmented by morpheme, but also the Mandarin data is split into characters.

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Authors and Affiliations

  1. Manifold Lab, OPPO Research Institute, Beijing, China

    Tingxun Shi, Qian Zhang, Xiaoxue Wang, Xiaopu Li, Zhengshan Xue & Jie Hao

Authors
  1. Tingxun Shi
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  2. Qian Zhang
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  3. Xiaoxue Wang
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  4. Xiaopu Li
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  5. Zhengshan Xue
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  6. Jie Hao
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Corresponding author

Correspondence to Zhengshan Xue .

Editor information

Editors and Affiliations

  1. Soochow University, Suzhou, China

    Junhui Li

  2. Dublin City University, Dublin, Ireland

    Andy Way

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Shi, T., Zhang, Q., Wang, X., Li, X., Xue, Z., Hao, J. (2020). Description and Findings of OPPO’s Machine Translation Systems for CCMT 2020. In: Li, J., Way, A. (eds) Machine Translation. CCMT 2020. Communications in Computer and Information Science, vol 1328. Springer, Singapore. https://doi.org/10.1007/978-981-33-6162-1_8

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  • DOI: https://doi.org/10.1007/978-981-33-6162-1_8

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  • Online ISBN: 978-981-33-6162-1

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