Sequence generation with reinforcement learning (RL) has received significant attention recently. However, a challenge with such methods is the sparse-reward problem in the RL training process, in which a scalar guiding signal is often only available after an entire sequence has been generated. This type of sparse reward tends to ignore the global structural information of a sequence, causing generation of sequences that are semantically inconsistent. In this paper, we present a model-based RL approach to overcome this issue. Specifically, we propose a novel guider network to model the sequence-generation environment, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments show that the proposed method leads to improved performance for both unconditional and conditional sequence-generation tasks.
@article{arxiv.1811.00696,
title = {Sequence Generation with Guider Network},
author = {Ruiyi Zhang and Changyou Chen and Zhe Gan and Wenlin Wang and Liqun Chen and Dinghan Shen and Guoyin Wang and Lawrence Carin},
journal= {arXiv preprint arXiv:1811.00696},
year = {2018}
}