Generating Questions Based on Semi-Automated and End-to-End Neural Network

• 作者: Tianci Xia Wei Song Xiaobing Zhao Yuan Sun Yumiao Guo
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 文学
• 关键词: GENERATING QUESTIONS semi-automated model END-TO-END neural network QUESTION answering
• 年，卷（期）: 2019,（8）
• 页码范围: 617-628
• 页数: 12页
• 分类号: H31

摘要

With the emergence of large-scale knowledge base,how to use triple information to generate natural questions is a key technology in question answering systems.The traditional way of generating questions require a lot of manual intervention and produce lots of noise.To solve these problems,we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions.The semi-automated model can generate question templates and real questions combining the knowledge base and center graph.The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network.Meanwhile,the attention mechanism is utilized in the decoding layer,which makes the triples and generated questions more relevant.Finally,the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.