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

Shengquan Wang

TL;DR

This work addresses text generation by marrying a deep Transformer language model with a GAN framework to enable semi-supervised learning through data augmentation. It uses a 24-layer GPT-2-like autoregressive model, a GAN with a minimax objective approximating the Jensen-Shannon divergence, and the Gumbel-Softmax reparameterization to handle discrete token outputs. Theoretical contributions include convergence analysis for the GAN objective, formal treatment of Gumbel-Softmax, and a semi-supervised learning protocol that combines synthetic text with real data to fine-tune the Transformer on an augmented dataset. The approach demonstrates that deeper models plus GAN-generated synthetic data can improve language modeling metrics, offering a pragmatic path to leverage unlabeled text when labeled data is scarce, with implications for more data-efficient text generation and augmentation pipelines. The significance lies in providing a principled framework to train discrete-sequence generators using gradient-based optimization, while highlighting challenges such as mode collapse and suggesting future extensions to sequence-focused GAN variants and reinforcement learning techniques.

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.

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

TL;DR

This work addresses text generation by marrying a deep Transformer language model with a GAN framework to enable semi-supervised learning through data augmentation. It uses a 24-layer GPT-2-like autoregressive model, a GAN with a minimax objective approximating the Jensen-Shannon divergence, and the Gumbel-Softmax reparameterization to handle discrete token outputs. Theoretical contributions include convergence analysis for the GAN objective, formal treatment of Gumbel-Softmax, and a semi-supervised learning protocol that combines synthetic text with real data to fine-tune the Transformer on an augmented dataset. The approach demonstrates that deeper models plus GAN-generated synthetic data can improve language modeling metrics, offering a pragmatic path to leverage unlabeled text when labeled data is scarce, with implications for more data-efficient text generation and augmentation pipelines. The significance lies in providing a principled framework to train discrete-sequence generators using gradient-based optimization, while highlighting challenges such as mode collapse and suggesting future extensions to sequence-focused GAN variants and reinforcement learning techniques.

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.

Paper Structure

This paper contains 19 sections, 1 theorem, 15 equations, 1 table.

Table of Contents

  1. Introduction
  2. Transformer (GPT-2) Language Modeling
  3. Auto-Regressive Objective
  4. GPT-2 Architecture and Model Capacity
  5. GAN for Text Generation
  6. Minimax Objective and Jensen-Shannon Divergence
  7. Practical Loss Definitions
  8. Discriminator Loss.
  9. Generator Loss.
  10. Gumbel-Softmax for Discrete Outputs
  11. Theoretical Analysis and Derivations
  12. Convergence Analysis of the GAN Objective
  13. Properties and Derivation of the Gumbel-Softmax
  14. Semi-Supervised Learning via Data Augmentation
  15. Synthetic Text Generation
  16. ...and 4 more sections

Key Result

Lemma 1

Let $\mathbf{u} \in \mathbb{R}^K$ be logits for a categorical distribution with $K$ classes, and let $g_i$ be i.i.d. samples from $\mathrm{Gumbel}(0,1)$. Then for temperature $\tau>0$, As $\tau \to 0$, $\mathbf{y}$ becomes nearly one-hot, while gradients remain continuous with respect to the logits $\mathbf{u}$.

Theorems & Definitions (1)

  • Lemma 1: Gumbel-Softmax Reparameterization