Research Area:  Machine Learning

Abstract:

In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text. In particular, we propose to learn neural rewards to model cross-sentence ordering as a means to approximate desired discourse structure. Empirical results demonstrate that a generator trained with the learned reward produces more coherent and less repetitive text than models trained with cross-entropy or with reinforcement learning with commonly used scores as rewards.

Keywords:  
Discourse-Aware Neural Rewards
Coherent Text Generation
Machine Learning
Deep Learning

Author(s) Name:  Antoine Bosselut, Asli Celikyilmaz, Xiaodong He, Jianfeng Gao, Po-Sen Huang, Yejin Choi

Publisher name:  arxiv

DOI:  arXiv:1805.03766

Volume Information:  

Paper Link:   https://arxiv.org/abs/1805.03766