A Neural Question Generation System Based on Knowledge Base

摘要：

　　Most of question-answer pairs in question answering task are generated manually,which is inefficient and expensive.However,the existing work on automatic question generation is not good enough to replace manual annotation.This paper presents a system to generate questions from a knowledge base in Chinese.The contribution of our work contains two parts.First we offer a neural generation approach using long short term memory(LSTM).Second,we design a new format of input sequence for the system,which promotes the performance of the model.On the evaluation of KBQG of NLPCC 2018 Shared Task 7,our system achieved 73.73 BLEU,and took the first place in the evaluation.

关键词： Question answering Generation Knowledge base

作者: Hao Wang Xiaodong Zhang Houfeng Wang

作者单位: MOE Key Lab of Computational Linguistics,Peking University,Beijing 100871,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: 133-142

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）