@inproceedings{zhao-etal-2021-ji,
    title = "基于枢轴语言系统融合的词汇混淆网络神经机器翻译(Neural Machine Translation for Vocabulary Confusion Network Based on Pivotal Language System Fusion)",
    author = "Zhao, Xiaobing  and
      Jin, Bo  and
      Sun, Yuan",
    editor = "Li, Sheng  and
      Sun, Maosong  and
      Liu, Yang  and
      Wu, Hua  and
      Liu, Kang  and
      Che, Wanxiang  and
      He, Shizhu  and
      Rao, Gaoqi",
    booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2021",
    address = "Huhhot, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.ccl-1.5/",
    pages = "46--56",
    language = "zho",
    abstract = "神经机器翻译在低资源语言的翻译任务中存在翻译难度大、译文质量不佳的问题。本文针对低资源语言与汉语之间没有双语平行语料的情况,采用正反向枢轴翻译的方法,生成了三种低资源语言到汉语的平行句对,采用词汇级的系统融合技术,将Transformer模型和对偶学习模型翻译生成的目标语言译文进行融合,然后通过混淆神经网络进行词汇选择,生成了更为优质的目标语言译文。实验证明,本文提出的多模型融合方法在爱沙尼亚语-汉语、拉脱维亚语-汉语、罗马尼亚语-汉语这三种低资源语言翻译任务中均优于独立模型的翻译效果,进一步提升了低资源语言神经机器翻译的译文质量。"
}