Abstract：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.
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