计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 272-278.doi: 10.11896/jsjkx.201200208

所属专题: 自然语言处理 虚拟专题

• 人工智能 • 上一篇    下一篇

基于产品建模的评论问题生成研究

肖康, 周夏冰, 王中卿, 段湘煜, 周国栋, 张民   

  1. 苏州大学计算机科学与技术学院 江苏 苏州215006
  • 收稿日期:2020-12-23 修回日期:2021-06-08 出版日期:2022-02-15 发布日期:2022-02-23
  • 通讯作者: 周夏冰(zhouxiabing@suda.edu.cn)
  • 作者简介:20184227061@stu.suda.edu.cn
  • 基金资助:
    国家自然科学基金青年科学基金项目(61806137,61702518)

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).

摘要: 问题自动生成是自然语言处理领域的一个研究热点,旨在从文本中生成自然问句。随着电子商务的不断发展,网络上产生了大量关于产品的评论。面对海量的评价信息,如何快速挖掘产品信息相关的关键评价,从而生成与产品各个层面息息相关的问答数据具有极大的研究价值,这对商家和顾客都具有极大的意义。现有的问题生成模型大多针对阅读理解类型等长文本语料,采用端到端序列化生成模型。然而,针对基于产品评论等短文本的问题生成任务,现有的模型无法将用户和商家重点关注的商品特性纳入学习过程。为了使生成的问题更加符合商品的特性,文中提出了基于产品建模的评论问题生成模型,通过与产品属性识别进行联合学习训练,使模型在解码层面加强了对特征信息的关注。与现有的问题生成模型相比,该模型不仅能解决产品数据口语化严重的问题,还能加强产品属性的识别能力,从而使生成的问题更加具体,更符合商品的特征。文中在京东与亚马逊产品评论数据集上同时进行实验,结果表明,在基于评论等短文本生成问题的任务上,与目前已有的问题生成模型相比,所提模型取得了较大的性能提升。基于中文京东数据集的实验中,所提模型的BLEU值提升了3.26%,ROUGE值提升了2.33%;基于英文亚马逊数据集的实验中,所提模型的BLEU值提升了2.01%,ROUGE值提升了2.10%。

关键词: 联合学习, 问题生成, 指针模型, 属性抽取, 注意力机制

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

中图分类号: 

  • TP391.1
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