An Understanding of Learning from Demonstrations for Neural Text Generation

Anonymous

Keywords: Text Generation, Reinforcement Learning, Learning from Demonstrations

Abstract:

In this blog post, we will go over the ICLR 2021 paper titled Text Generation by Learning from Demonstrations. This paper introduces a learning method based on offline, off-policy reinforcement learning (RL) which addresses two key limitations of a training objective used in neural text generation models: Maximum Likelihood Estimate (MLE).

Goal of this blog post: Our main goal with this blog post is to provide researchers and practitioners in both NLP and RL with (1) a better understanding of algorithm presented in this paper (GOLD), and (2) an understanding of how RL is used for text generation.

ICLR Paper: https://arxiv.org/pdf/2009.07839.pdf