Reinforcement Learning for Few-Shot Text Generation Adaptation

• URL: http://arxiv.org/abs/2111.11030v1
• Date: Mon, 22 Nov 2021 07:33:40 GMT
• Title: Reinforcement Learning for Few-Shot Text Generation Adaptation
• Authors: Cheng Pengsen, Dai Jinqiao, Liu Jiayong
• Abstract summary: We frame the adaptation of text generation systems as a reinforcement learning problem. We show that our method significantly outperforms domain adaptation when very few in-domain samples are available.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controlling the generative model to adapt a new domain with limited samples is a difficult challenge and it is receiving increasing attention. Recently, few-shot learning has shown promising process in domain adaptation. However, the texts generated by few-shot learning are typically devoid of linguistic diversity. To address this shortcoming, we frame the adaptation of text generation systems as a reinforcement learning problem and provide a new approach to make text generation models easily adaptable to target domain with the minimal amount of in-domain data. Experimental results on five target domains in two few-shot configurations demonstrate that our method significantly outperforms domain adaptation when very few in-domain samples are available.

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