
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. 

DL-based CTG Methods Pros 


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

DL-based CTG Methods Cons

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. 

University of California, Berkeley

An area within Natural Language Generation, Controllable Text Generation is integral in developing text generation technologies that are more natural and customizable in practical applications. CTG is the task of generating text given a controlled element. 

Controllable Text Generation (CTG) 


An NLG system should generate text that meet task-specific, controllable constraints. In dialogue response generation, one would control for politeness / offensive content. 

Controllability in Text Generation 


Deep Learning-Based CTG Methods 


The CTG can be described as 
$P(Y|C) = p(y_1, y_2, …, y_n|C$ 
Where C varies according to the task. Additionally, the generated text Y should satisfy the constraint conditions while being reasonably human-readable. 

Formal Definition of CTG 

A CTG system has 3 components:

(1) The controlled element, which includes a controlled condition and source text 


(2) The generative model, as process P


(3) The generated text satisfying the input control condition, as output O. 

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