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Adversarial Feature Matching for Text Generation
Yizhe Zhang · Zhe Gan · Kai Fan · Zhi Chen · Ricardo Henao · Dinghan Shen · Lawrence Carin

Tue Aug 08 09:42 PM -- 10:00 PM (PDT) @ Parkside 1

The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a convolutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric. This eases adversarial training by alleviating the mode-collapsing problem. Our experiments show superior performance in quantitative evaluation, and demonstrate that our model can generate realistic-looking sentences.

Author Information

Yizhe Zhang (Duke university)
Zhe Gan (Duke University)
Kai Fan
Zhi Chen (Nanjing University)
Ricardo Henao (Duke University)
Dinghan Shen (Duke University)
Lawrence Carin (Duke)

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