ChenChengKuan/awesome-text-generation

Organizes foundational text generation architectures—GANs, VAEs, autoencoders, and reinforcement learning approaches—alongside downstream applications like image-to-text, dialogue, and style transfer. Covers both the generative models themselves and their specific implementations across diverse NLP tasks, with direct links to papers and code repositories. Structured as a taxonomy of techniques rather than a flat list, enabling researchers to trace how different architectural choices map to particular generation problems.

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