Gossiping the Videos:An Embedding-Based Generative Adversarial Framework for Time-Sync Comments Generation

摘要：

　　Recent years have witnessed the successful rise of the timesync “gossiping comment,or so-called “Danmu combined with online videos.Along this line,automatic generation of Danmus may attract users with better interactions.However,this task could be extremely challenging due to the difficulties of informal expressions and “semantic gap between text and videos,as Danmus are usually not straightforward descriptions for the videos,but subjective and diverse expressions.To that end,in this paper,we propose a novel Embedding-based Generative Adversarial(E-GA)framework to generate time-sync video comments with “gossiping behavior.Specifically,we first model the informal styles of comments via semantic embedding inspired by variational autoencoders(VAE),and then generate Danmus in a generatively adversarial way to deal with the gap between visual and textual content.Extensive experiments on a large-scale real-world dataset demonstrate the effectiveness of our E-GA framework.

作者: Guangyi Lv Tong Xu Qi Liu Enhong Chen Weidong He Mingxiao An Zhongming Chen

作者单位: Anhui Province Key Laboratory of Big Data Analysis and Application,School of Computer Science and Te Quantum Lab,Research Institute of OPPO,Shanghai,China

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 412-424

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）