Expert-based reward function training: the novel method to train sequence generators
Joji Toyama · Yusuke Iwasawa · Kotaro Nakayama · Yutaka Matsuo
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.
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