Expert-based reward function training: the novel method to train sequence generators

Joji Toyama, Yusuke Iwasawa, Kotaro Nakayama, Yutaka Matsuo

Feb 12, 2018 (modified: Jun 04, 2018) ICLR 2018 Workshop Submission readers: everyone Show Bibtex
  • Abstract: The training methods of sequence generator with a combination of GAN and policy gradient has shown good performance. In this paper, we propose expert-based reward function training: the novel method to train sequence generator. Different from previous studies of sequence generation, expert-based reward function training does not utilize GAN's framework. Still, our model outperforms SeqGAN and a strong baseline, RankGAN.
  • TL;DR: This paper aims to learn a better metric for unsupervised learning, such as text generation, and shows a significant improvement over SeqGAN.
  • Keywords: sequence generation, reinforcement learning, unsupervised learning, RNN
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