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Reinforcement Learning for Few-Shot Text Generation Adaptation

arXiv 2111.11030 ↗First posted 22 Nov 2021cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 39 of its 55 references that are in the map. First posted on arXiv 22 Nov 2021.

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27 more
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