Reinforcement Learning for Few-Shot Text Generation Adaptation [article]

Pengsen Cheng, Jinqiao Dai, Jiamiao Liu, Jiayong Liu, Peng Jia
2022 arXiv   pre-print
Controlling the generative model to adapt a new domain with limited samples is a difficult challenge and it is receiving increasing attention. Recently, methods based on meta-learning have shown promising results for few-shot domain adaptation. However, meta-learning-based methods usually suffer from the problem of overfitting, which results in a lack of diversity in the generated texts. To avoid this problem, in this study, a novel framework based on reinforcement learning (RL) is proposed. In
more » ... this framework, to increase the sample utilization of RL and decrease its sample requirement, maximum likelihood estimation learning is incorporated into the RL process. When there are only a few in-domain samples available, experimental results on five target domains in two few-shot configurations show that this framework performs better than baselines.
arXiv:2111.11030v3 fatcat:lofyv3nsnjf4bihakn6hut6yby