
DL-based methods are capable of data-driven, end-to-end learning of low-dim dense vectors that represent the linguistic features of text. This alleviates the data sparsity issue and avoids bias of hand-crafted features. 

University of California, Berkeley

Early approaches were based on sequential methods and style embedding, which was followed by deep generative model-based methods such as Variational Autoencoders (VAEs), Generative Adversarial Nets (GANs), and Energy-based Models. 

Deep Learning-Based CTG Methods 


DL-based CTG Methods Pros 


DL-based methods require large-scale datasets, which makes supervised and cross-domain text generation tasks challenging. 

Learn Before

Related