石航,刘瑞芳,刘欣瑜,陈泓宇. 基于文章和近答案句信息的问题生成模型[J]. 中文信息学报, 2021, 35(8): 127-134.
SHI Hang, LIU Ruifang, LIU Xinyu, CHEN Hongyu. Question Generation Based on Paragraph and Close-Answer Context. , 2021, 35(8): 127-134.
基于文章和近答案句信息的问题生成模型
石航,刘瑞芳,刘欣瑜,陈泓宇
北京邮电大学 人工智能学院,北京 100876
Question Generation Based on Paragraph and Close-Answer Context
SHI Hang, LIU Ruifang, LIU Xinyu, CHEN Hongyu
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Abstract：The automatic question generation task aims to generate natural language questions for a paragraph of text. Existing neural question generation models mainly focused on using one sentence or the whole paragraph with target answer as the input. To better utilize the context of the target answer, this paper proposes a multi-input hierarchical attention sequence to sequence network to capture more valuable sentence information and richer semantic information of paragraph to generate high-quality questions. Experiments on SQuAD show that our method is better than the state-of-the-art in terms of BLEU4, and the response rate of this method is obviously better than the baseline system.
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2021, Vol. 35 
