Reinforcement Learning for Few-Shot Text Generation Adaptation

TL;DR

This paper introduces a reinforcement learning framework combined with maximum likelihood estimation to improve few-shot text generation adaptation, addressing overfitting and diversity issues in low-data scenarios.

Contribution

A novel RL-based framework that effectively leverages limited samples for domain adaptation in text generation, outperforming existing methods.

Findings

Outperforms baselines in five target domains

Reduces overfitting and enhances diversity

Effective with very few samples

Abstract

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.