A Question Generation Method Based on Subgraph Paraphrase
This paper proposes a method based on subgraph rephrasing to solve the problem of unseen predicates in question generation over knowledge graph.Traditional KBQG(Question Generation over Knowledge Base)methods main-ly use annotated Q&A(Question and Answer)data(question and logic formal pairs)to generate questions.However,anno-tated data can't fully cover all predicates in the knowledge graph.It is still a challenge to generate questions with unseen predicates in the knowledge graph.In this paper,we propose a semantic decoupling method based on subgraph structure.By decomposing the subgraph corresponding to a complex question into atomic subgraphs,the multi-hop subgraph containing unseen predicates can be divided into single-hop subgraphs that are easy to handle.In addition,we design a subgraph re-phrasing procedure to train a subgraph rewriter on large-scale unsupervised data through sampling the predicates in the data-set by subgraph sampling.The subgraph rewriter will provide natural language form for subgraphs and effective information for generating questions.This paper quantitatively analyzes the performance of the model at different difficulty levels.The experimental results on GrailQA and other datasets show that our method achieves the state-of-the-art performance.
万方数据
维普
