基于GraphTransformer的知识库问题生成

知识库问答依靠知识库推断答案,需要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,该任务的特点是利用知识库三元组自动生成问题,但现有方法仅由一个三元组生成的问题过于简短,且缺乏多样性。为生成信息量丰富且多样化的问题,该文采用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数据集上的实验结果证明了该方法的有效性。

Question Generation from Knowledge Base with Graph Transformer

Knowledge base question answering requires a large number of question answering pairs. To alleviate the problem of data annotation, the question generation from knowledge base has attracted the attention of researchers. This task is to use the triples of knowledge base to automatically generate the questions. To generate questions with rich and diverse information, this paper uses two encoding layers, Graph Transformer and BERT, to enhance the multi-granular semantic representation of triples to obtain background information. Experimental results on the SimpleQuestions dataset prove the effectiveness of the method.