Journal of Frontiers of Computer Science and Technology ›› 2020, Vol. 14 ›› Issue (8): 1389-1396.DOI: 10.3778/j.issn.1673-9418.1908075

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Considering Grade Information for Music Comment  Text Automatic Generation

YAN Dan, HE Jun, LIU Hongyan, DU Xiaoyong   

  1. 1. Key Laboratory of Data Engineering and Knowledge Engineering (School of Information, Renmin University of China), Ministry of Education, Beijing 100872, China
    2. School of Economics and Management, Tsinghua University, Beijing 100084, China
  • Online:2020-08-01 Published:2020-08-07



  1. 1. 数据工程与知识工程教育部重点实验室(中国人民大学 信息学院),北京 100872
    2. 清华大学 经济管理学院,北京 100084


Online singing platforms as a new type of entertainment attract huge number of users in recent years. On online singing platform, writing comments for published music is a way for users to share their music pieces and communicate with each other, which has an incentive effect on users to publish music. However, there is usually no comment for a new user's music or a newly released music. Automatic generating comments for these music pieces can solve this problem to some extent. There is a certain relationship between the comments and the performance grade of the music on the online singing platform. Therefore, this paper studies how to automatically generate comments for published music considering its performance level information. To this end, this paper proposes a deep text generation model based on generative adversarial network, called GradeGAN. Major components of this model include generators, text discriminator and grade discriminators, where both text discriminator and grade discriminator are used to guide the generator to generate accurate text and make the generated text match their grades. Experiment conducted on real data sets shows that compared with existing models, the proposed model not only has higher accuracy, but also has high diversity in generating comments.

Key words: automatic text generation, generative adversarial net (GAN), reinforcement learning, deep learning



关键词: 文本自动生成, 生成式对抗网络(GAN), 强化学习, 深度学习