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Reinforcement Learning for Few-Shot Text Generation Adaptation
[article]
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
arXiv:2111.11030v3
fatcat:lofyv3nsnjf4bihakn6hut6yby