用强化学习生成假文本，提升小样本文本分类效果
READ: Reinforcement-based Adversarial Learning for Text Classification with Limited Labeled Data
- 用强化学习从无标签数据生成多样合成文本
- 在多个数据集上超越现有最先进方法
- 适合标注数据稀缺的文本分类任务
预训练的Transformer模型（如BERT）在众多文本分类任务中表现优异，但通常需要大量标注数据才能达到良好性能。获取标注数据成本高、耗时长，而通过某些启发式方法收集未标注数据则相对廉价。为此，本文提出一种新方法READ（基于强化学习的对抗学习），将强化学习驱动的文本生成与半监督对抗学习相结合，利用未标注数据生成多样化合成文本，通过对抗学习提升模型泛化能力。实验结果表明，READ在多个数据集上均优于现有最先进方法。
原文摘要 · Abstract (English)
Pre-trained transformer models such as BERT have shown massive gains across many text classification tasks. However, these models usually need enormous labeled data to achieve impressive performances. Obtaining labeled data is often expensive and time-consuming, whereas collecting unlabeled data using some heuristics is relatively much cheaper for any task. Therefore, this paper proposes a method that encapsulates reinforcement learning-based text generation and semi-supervised adversarial learning approaches in a novel way to improve the model's performance. Our method READ, Reinforcement-based Adversarial learning, utilizes an unlabeled dataset to generate diverse synthetic text through reinforcement learning, improving the model's generalization capability using adversarial learning. Our experimental results show that READ outperforms the existing state-of-art methods on multiple datasets.
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