Controllable Natural Language Generation with Contrastive Prefixes

Jing Qian, Li Dong, Yelong Shen, Furu Wei, Weizhu Chen


Abstract
To guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator. In this work, we propose a novel lightweight framework for controllable GPT2 generation, which utilizes a set of small attribute-specific vectors, called prefixes (Li and Liang, 2021), to steer natural language generation. Different from Li and Liang (2021), where each prefix is trained independently, we take the relationship among prefixes into consideration and train multiple prefixes simultaneously. We propose a novel supervised method and also an unsupervised method to train the prefixes for single-aspect control while the combination of these two methods can achieve multi-aspect control. Experimental results on both single-aspect and multi-aspect control show that our methods can guide generation towards the desired attributes while keeping high linguistic quality.
Anthology ID:
2022.findings-acl.229
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2912–2924
Language:
URL:
https://aclanthology.org/2022.findings-acl.229
DOI:
10.18653/v1/2022.findings-acl.229
Bibkey:
Cite (ACL):
Jing Qian, Li Dong, Yelong Shen, Furu Wei, and Weizhu Chen. 2022. Controllable Natural Language Generation with Contrastive Prefixes. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2912–2924, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Controllable Natural Language Generation with Contrastive Prefixes (Qian et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.229.pdf
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