@inproceedings{nie-etal-2022-ji,
    title = "基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)",
    author = "Nie, Shuang  and
      Ye, Zheng  and
      Qin, Jun  and
      Liu, Jing",
    editor = "Sun, Maosong  and
      Liu, Yang  and
      Che, Wanxiang  and
      Feng, Yang  and
      Qiu, Xipeng  and
      Rao, Gaoqi  and
      Chen, Yubo",
    booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Nanchang, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.ccl-1.51/",
    pages = "569--579",
    language = "zho",
    abstract = "``目前常见的机器阅读理解数据增强方法如回译,单独对文章或者问题进行数据增强,没有考虑文章、问题和选项三元组之间的联系。因此,本文探索了一种利用三元组联系进行文章句子筛选的数据增强方法,通过比较文章与问题以及选项的相似度,选取文章中与二者联系紧密的句子。同时为了使不同选项的三元组区别增大,我们选用了正则化Dropout的策略。实验结果表明,在RACE数据集上的准确率可提高3.8{\%}。''"
}