An Analytical and Comparative Review of Deep Learning-Based Sequence Generation Models

Abstract

Sequence generation has become a central problem in deep learning with applications in natural language processing, speech synthesis, and time-series analysis. This paper presents a structured analytical review of three major deep learning–based sequence generation paradigms: Long Short-Term Memory (LSTM) networks, Generative Adversarial Networks (GANs), and SeqGAN. The study compares these models in terms of learning mechanism, handling of discrete and continuous data, computational complexity, training stability, and sequence-level optimization capability. The analysis demonstrates that LSTM models provide stable probabilistic learning but suffer from exposure bias, while GANs achieve powerful distribution modeling yet face instability and limitations in discrete sequence generation. SeqGAN addresses this limitation by integrating reinforcement learning and policy gradient optimization, enabling adversarial training for discrete sequences at the cost of higher computational cost and variance in training. The paper further identifies the theoretical trade-offs among these approaches and discusses their suitability for different sequence generation tasks. Finally, future research directions involving transformer-based architectures and hybrid adversarial-reinforcement learning models are highlighted.