@inproceedings{hu-zhou-2020-ji,
    title = "基于Graph Transformer的知识库问题生成(Question Generation from Knowledge Base with Graph Transformer)",
    author = "Hu, Yue  and
      Zhou, Guangyou",
    editor = "Sun, Maosong  and
      Li, Sujian  and
      Zhang, Yue  and
      Liu, Yang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.ccl-1.31/",
    pages = "324--335",
    language = "zho",
    abstract = "知识库问答依靠知识库推断答案需大量带标注信息的问答对,但构建大规模且精准的数据集不仅代价昂贵,还受领域等因素限制。为缓解数据标注问题,面向知识库的问题生成任务引起了研究者关注,该任务是利用知识库三元组自动生成问题。现有方法仅由一个三元组生成的问题简短且缺乏多样性。为生成信息量丰富且多样化的问题,本文采用Graph Transformer和BERT两个编码层来加强三元组多粒度语义表征以获取背景信息。在SimpleQuestions上的实验结果证明了该方法有效性。"
}