SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient (1609.05473v6)

Abstract: As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.

An Expert Overview of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient"

"SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient" by Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu, represents a noteworthy contribution to the domain of sequence generation, particularly extending the capabilities of Generative Adversarial Networks (GANs) to discrete data sequences. This paper addresses two critical challenges inherent in applying GANs to sequence generation: the difficulty of passing gradient updates from the discriminative model to the generative model due to the discrete nature of output tokens and the issue of evaluating intermediate, partially generated sequences.

Key Approach and Architecture

The authors propose a framework called SeqGAN, which incorporates GANs into sequence generation using methodologies from reinforcement learning (RL). In this architecture, the generative model is cast as a stochastic policy in an RL setting, thus sidestepping the differentiation problem of discrete outputs by utilizing policy gradient updates. A GAN discriminator serves as the reward signal for the RL, evaluating the quality of complete sequences and guiding the generator.

The key idea is to employ Monte Carlo search to approximate the expected reward for intermediate states. This enables backpropagation of the reward signal from the full sequence evaluation to earlier actions within the sequence, thus addressing the challenge of intermediate sequence assessment.

Experimental Evaluation and Results

To rigorously test the efficacy of SeqGAN, the authors performed extensive experiments using both synthetic data and real-world tasks. On the synthetic data front, they used a LLM to generate training data and compared SeqGAN's performance against several strong baselines: Maximum Likelihood Estimation (MLE), Scheduled Sampling (SS), and PG-BLEU. The results demonstrated that SeqGAN significantly outperforms these baselines, specifically in terms of the negative log-likelihood (NLL), clearly illustrating its superior ability to generate high-quality sequences.

In real-world tasks such as Chinese poem generation, Barack Obama political speech generation, and music generation, SeqGAN also displayed notable improvements. For Chinese poem composition, SeqGAN achieved a BLEU-2 score of 0.7389 and performed comparably to human-generated content based on expert evaluations. Likewise, in generating Obama political speeches and music, SeqGAN outperformed MLE significantly in both BLEU scores and Mean Squared Error (MSE), confirming its robustness and ability to cope with various data types.

Theoretical and Practical Implications

The advancements proposed in SeqGAN hold substantial theoretical and practical implications:

1. Reinforcement Learning Integration: The effective use of RL principles in updating GANs for sequence generation underscores a versatile approach that could be extended to other complex tasks involving sequential data.
2. Monte Carlo Search: The introduction of Monte Carlo search for intermediate sequence evaluation offers a powerful technique to approximate state-action values, potentially influencing future methods in sequence modeling and other RL applications.
3. Versatility Across Domains: By proving efficacy in tasks ranging from natural language processing to music generation, SeqGAN sets a precedent for general-purpose sequence-generative models adaptable to various domains.
4. Performance Metrics: The application of robust evaluation metrics like BLEU-2 and NLL provides a clear benchmark for future research, ensuring that advancements can be quantitatively measured and compared.

Future Directions

While SeqGAN demonstrates significant progress, there are multiple directions for future research:

• Scalability: Extending SeqGAN to handle longer and more complex sequences remains an open challenge. Techniques such as Monte Carlo Tree Search and advanced value networks could be investigated to improve decision-making capabilities.
• Intermediate Reward Signals: Developing more sophisticated discriminators capable of providing intermediate rewards for partially generated sequences could further boost SeqGAN's training stability and performance.
• Diverse Applications: Beyond poetry, speech, and music, exploring SeqGAN's application in areas such as genetic sequence modeling, interactive storytelling, and automated software code generation could reveal additional strengths and areas for improvement.

In summary, "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient" marks a pivotal step in the intersection of GANs and sequence generation. By leveraging policy gradients and Monte Carlo search, the authors provide a robust framework that not only addresses fundamental challenges but also opens pathways for future research and practical applications across various domains involving discrete sequential data.