Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator

Guisheng Liu, Yi Li, Yanqing Guo, Xiangyang Luo, Bo Wang


Abstract
Though existing researches have achieved impressive results in controlled text generation, they focus mainly on single-attribute control. However, in applications like automatic comments, the topic and sentiment need to be controlled simultaneously. In this work, we propose a new framework for multi-attribute controlled text generation. To achieve this, we design a contrastive-generator that can effectively generate texts with more attributes. In order to increase the convergence of the text on the desired attributes, we adopt an external-discriminator to distinguish whether the generated text holds the desired attributes. Moreover, we propose top-n weighted decoding to further improve the relevance of texts to attributes. Automated evaluations and human evaluations show that our framework achieves remarkable controllability in multi-attribute generation while keeping the text fluent and diverse. It also yields promising performance on zero-shot generation.
Anthology ID:
2022.coling-1.516
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5904–5913
Language:
URL:
https://aclanthology.org/2022.coling-1.516
DOI:
Bibkey:
Cite (ACL):
Guisheng Liu, Yi Li, Yanqing Guo, Xiangyang Luo, and Bo Wang. 2022. Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5904–5913, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (Liu et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.516.pdf
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