  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 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.

Computation and Language