Neural Question Generation with Semantics of Question Type

摘要：

　　This paper focuses on automatic question generation(QG)that transforms a narrative sentence into an interrogative sentence.Recently,neural networks have been used in this task due to its extraordinary ability of semantics encoding and decoding.We propose an approach which incorporates semantics of the possible question type.We utilize the Convolutional Neural Network(CNN)for predicting question type of the answer phrases in the narrative sentence.In order to incorporate the question type semantics into the generating process,we classify the question type which the answer phrases refer to.In addition,We use Bidirectional Long Short Term Memory(Bi-LSTM)to construct the question generating model.The experiment results show that our method outperforms the baseline system with the improvement of 1.7%on BLEU-4 score and beyonds the state-of-the-art.

关键词： Question generation Question type Answer phrases

作者: Xiaozheng Dong Yu Hong Xin Chen Weikang Li Min Zhang Qiaoming Zhu

作者单位: School of Computer Science and Technology of Jiangsu Province,Soochow University,Suzhou 215006,Jiangsu,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 213-223

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）