Generative Adversarial Network and its Applications to Human Language Processing

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IEEE Signal Processing Society Chapter Santa Clara Valley

Abstract:
Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, but the applications of GAN on text and speech processing are still limited. In this talk, I will demonstrate the applications of GAN on unsupervised abstractive summarization and sentiment controllable chat-bot. I will also talk about the research directions towards unsupervised speech recognition by GAN.
 Biography:
Hung-yi Lee received the M.S. and Ph.D. degrees from National Taiwan University (NTU), Taipei, Taiwan, in 2010 and 2012, respectively. From September 2012 to August 2013, he was a postdoctoral fellow in Research Center for Information Technology Innovation, Academia Sinica. From September 2013 to July 2014, he was a visiting scientist at the Spoken Language Systems Group of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He is currently an assistant professor of the Department of Electrical Engineering of National Taiwan University, with a joint appointment at the Department of Computer Science & Information Engineering of the university. His research focuses on machine learning (especially deep learning), spoken language understanding and speech recognition. He owns a YouTube channel teaching deep learning (in Mandarin) with more than 1.2M views and 20k subscribers (<A HREF="https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ/playlists" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ/playlists</A>).

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Generative Adversarial Network and its Applications to Human Language Processing

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Generative Adversarial Network and its Applications to Human Language Processing

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