@inproceedings{rui-etal-2024-rong,
    title = "融合确定性因子及区域密度的k-最近邻机器翻译方法(A k-Nearest-Neighbor Machine Translation Method Combining Certainty Factor and Region Density)",
    author = "Qi, Rui  and
      Shi, Xiangyu  and
      Man, Zhibo  and
      Xu, Jinan  and
      Chen, Yufeng",
    editor = "Maosong, Sun  and
      Jiye, Liang  and
      Xianpei, Han  and
      Zhiyuan, Liu  and
      Yulan, He",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/author-degibert/2024.ccl-1.16/",
    pages = "217--229",
    language = "zho",
    abstract = "``k-最近邻机器翻译(kNN-MT)是近年来神经机器翻译领域的一个重要研究方向。此类方法可以在不更新机器翻译模型的情况下提高翻译质量,但训练数据中高低频单词的数量不均衡限制了模型效果,且固定的k值无法对处于不同密度分布的数据都产生良好的翻译结果。为此本文提出了一种创新的kNN-MT方法,引入确定性因子(CF)来降低数据不均衡对模型效果的影响,并根据测试点周边数据密度动态选择k值。在多领域德-英翻译数据集上,相比基线实验,本方法在四个领域上翻译效果均有提升,其中三个领域上提升超过1个BLEU,有效提高了神经机器翻译模型的翻译质量。''"
}