VIP内容
自然语言生成经过几十年的发展，已经成为人工智能和自然语言处理的重要研究领域。最早的自然语言生成系统采用规则、模板的方法，设计各司其职的模块进行文本生成，其中体现了很多专家设计的词汇、语法、句法甚至语用的语言学知识。统计语言模型则从概率统计的角度提出了语言建模的新思路，将词汇与上下文的依赖关系编码在条件概率中。以深度学习模型为基本架构的现代语言生成模型绝大多数通过端到端训练的方式，能更好地建模词汇与上下文之间统计共现关系，显著地提升了文本生成的性能。特别是以Transformer为基础架构的预训练语言生成模型，能够较好地捕获包括词汇、语法、句法、语义等各层面的语言学知识，极大地推动了自然语言生成的进展，生成效果令人惊叹。
• 自然语言生成(NLG)是自然语言处理的一个子领域
• 专注于构建系统，自动生成连贯和有用的书面或口头文本供人类使用
• NLG系统已经在改变我们生活的世界……
【79页ppt】Neural Language Generation
热门内容
最新内容
最新论文
The impact of Universities on the social, economic and political landscape is one of the key directions in contemporary educational evaluation. In this paper, we discuss the new methodological technique that evaluates the impact of university based on popularity (number of page-views) of their alumni's pages on Wikipedia. It allows revealing the alumni popularity dynamics and tracking its state. Preliminary analysis shows that the number of page-views is higher for the contemporary persons that prove the perspectives of this approach. Then, universities were ranked based on the methodology and compared to the famous international university rankings ARWU and QS based only on alumni scales: for the top 10 universities, there is an intersection of two universities (Columbia University, Stanford University). The correlation coefficients between different university rankings are provided in the paper. Finally, the ranking based on the alumni popularity was compared with the ranking of universities based on the popularity of their webpages on Wikipedia: there is a strong connection between these indicators.
