Reinforcement Learning for Few-Shot Text Generation Adaptation
This work addresses the challenge of adapting generative models to new domains with limited data, which is important for applications like personalized content creation, but it appears incremental as it builds on existing reinforcement learning and meta-learning approaches.
The paper tackles the problem of few-shot domain adaptation for text generation by proposing a reinforcement learning framework that incorporates maximum likelihood estimation to reduce sample requirements and improve sample utilization. Experimental results on five target domains show that the framework outperforms baselines in few-shot configurations.
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.