Abstract
Recent years have witnessed the successful rise of the time-sync “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.
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Acknowledgments
This research was partially supported by grants from the National Natural Science Foundation of China (Grant No. 61727809, U1605251, 61672483, and 61703386), the Anhui Provincial Natural Science Foundation (Grant No. 1708085QF140), and the Fundamental Research Funds for the Central Universities (Grant No. WK2150110006).
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Lv, G. et al. (2019). Gossiping the Videos: An Embedding-Based Generative Adversarial Framework for Time-Sync Comments Generation. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_32
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