Exploring Controllable Text Generation Techniques

Shrimai Prabhumoye, Alan W Black, Ruslan Salakhutdinov


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.
Anthology ID:
2020.coling-main.1
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1–14
Language:
URL:
https://aclanthology.org/2020.coling-main.1
DOI:
10.18653/v1/2020.coling-main.1
Bibkey:
Cite (ACL):
Shrimai Prabhumoye, Alan W Black, and Ruslan Salakhutdinov. 2020. Exploring Controllable Text Generation Techniques. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1–14, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Exploring Controllable Text Generation Techniques (Prabhumoye et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.1.pdf