A Semi-Supervised Text Generation Framework Combining a Deep Transformer and a GAN

• URL: http://arxiv.org/abs/2502.05937v1
• Date: Sun, 09 Feb 2025 15:38:43 GMT
• Title: A Semi-Supervised Text Generation Framework Combining a Deep Transformer and a GAN
• Authors: Shengquan Wang,
• Abstract summary: This paper introduces a framework that connects a deep generative pre-trained Transformer language model with a generative adversarial network for text generation. The proposed model is first pre-trained unsupervised on a large and diverse text corpus with 24 layers. The paper also shows a semi-supervised approach where real data is augmented with GAN samples, which is further used to fine-tune the Transformer model on the merged dataset.
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• Abstract: This paper introduces a framework that connects a deep generative pre-trained Transformer language model with a generative adversarial network for semi-supervised text generation. In other words, the proposed model is first pre-trained unsupervised on a large and diverse text corpus with 24 layers. Then a simple GAN architecture for synthetic text generation is introduced, and Gumbel-Softmax is applied to handle the discreteness of tokens. The paper also shows a semi-supervised approach where real data is augmented with GAN samples, which is further used to fine-tune the Transformer model on the merged dataset. Detailed theoretical derivations are also included, outlining the proof of the min-max objective function, and an extensive discussion of the Gumbel-Softmax reparameterization trick.

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