Exploring Controllable Text Generation Techniques

COLING 2020

Exploring Controllable Text Generation Techniques

Jan 15, 2021
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Abstract: Neural controllable text generation is an important area gaining attention due to its plethora of applications. Although there is a large body of prior work in controllable text generation, there is no unifying theme. In this work, we provide a new schema of the pipeline of the generation process by classifying it into five modules. The control of attributes in the generation process requires modification of these modules. We present an overview of different techniques used to perform the modulation of these modules. We also provide an analysis on the advantages and disadvantages of these techniques. We further pave ways to develop new architectures based on the combination of the modules described in this paper. Abstract: Shrimai Prabhumoye, Alan W Black, Ruslan Salakhutdinov (Carnegie Mellon University)

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