Adversarial text generation with context adapted global knowledge and a self-attentive discriminator
作者：
Highlights：
• A word sequence-based adversarial network that exploits the semantics of the corpus by adapting global word embeddings to the context of analysis.
• Self-attentive discriminator to map the semantics of the generated text with real-world text.
• Evaluation framework based on quantitative and qualitative analyses.
• A word sequence-based adversarial network that balances both generator and discriminator towards reaching the Nash equilibrium.
摘要
•A word sequence-based adversarial network that exploits the semantics of the corpus by adapting global word embeddings to the context of analysis.•Self-attentive discriminator to map the semantics of the generated text with real-world text.•Evaluation framework based on quantitative and qualitative analyses.•A word sequence-based adversarial network that balances both generator and discriminator towards reaching the Nash equilibrium.
论文关键词：Text generation,Generative adversarial networks,Global knowledge,Self-attentive
论文评审过程：Received 7 April 2019, Revised 23 January 2020, Accepted 28 January 2020, Available online 12 March 2020, Version of Record 20 October 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102217
