Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 272-278.doi: 10.11896/jsjkx.201200208

Special Issue: Natural Language Processing

• Artificial Intelligence • Previous Articles     Next Articles

Review Question Generation Based on Product Profile

XIAO Kang, ZHOU Xia-bing, WANG Zhong-qing, DUAN Xiang-yu, ZHOU Guo-dong, ZHANG Min   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2020-12-23 Revised:2021-06-08 Online:2022-02-15 Published:2022-02-23
  • About author:XIAO Kang,born in 1993,postgra-duate,is a member of China Computer Federation.His main research interests include natural language processing and question generation.
    ZHOU Xia-bing,born in 1988,Ph.D,postgraduate supervisor,is a member of China Computer Federation.Her main research interests include natural language processing and emotion analysis.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(61806137,61702518).

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Abstract: Automatic question generation is a research hotspot in the field of natural language processing,which aims to generate natural questions from texts.With the continuous development of internet,a large amount of commodity reviews has been generated in the electronic commerce fields.In the face of massive review information,how to quickly mine key reviews related to pro-duct information has great research value.It is of great importance to both customers and merchants.Most of existing question generation models are based on reading comprehension type corpus and use sequence-to-sequence network to generate questions.However,for question generation tasks based on product reviews,existing models fail to incorporate the product information that users and businesses focus on into the learning process.In order to make the generated questions more in line with the attributes of the goods,a question generation model based on product is proposed in this paper.Through joint learning and training with product attribute recognition,the model strengthens the attention to feature information related to product.Compared with the existing question generation models,this model can not only strengthen the recognition ability of product attributes,but also ge-nerate contents more accurately.This paper carries out experiments on the data sets of product reviews of JD and Amazon.The results show that in the question generation task based on reviews,this model achieves a great improvement compared with the existing question generation model,which is improved by 3.26% and 2.01% respectively on BLEU,and 2.33% and 2.10% respectively on ROUGE.

Key words: Attention mechanism, Attribute extraction, Joint learning, Pointer model, Question generation

CLC Number: 

  • TP391.1
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