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Computer Science > Computation and Language

arXiv:2204.13362 (cs)
[Submitted on 28 Apr 2022]

Title:Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation

Authors:Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing Chen, Jun Xie
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Abstract:Attribute-based Controlled Text Generation (CTG) refers to generating sentences that satisfy desirable attributes (e.g., emotions and topics). Existing works often utilize fine-tuning or resort to extra attribute classifiers, yet suffer from storage and inference time increases. To address these concerns, we explore attribute-based CTG in a prompt-based manner. In short, the proposed Tailor represents each attribute as a pre-trained continuous vector (i.e., single-attribute prompt) and guides the generation of a fixed PLM switch to a pre-specified attribute. We experimentally find that these prompts can be simply concatenated as a whole to multi-attribute CTG without any re-training, yet raises problems of fluency decrease and position sensitivity. To this end, Tailor provides a multi-attribute prompt mask and a re-indexing position-ids sequence to bridge the gap between the training (one prompt for each task) and testing stage (concatenating more than one prompt). To further enhance such single-attribute prompt combinations, Tailor also introduces a trainable prompt connector, which can be concatenated with any two single-attribute prompts to multi-attribute text generation. Experiments on 11 attribute-specific generation tasks demonstrate strong performances of Tailor on both single-attribute and multi-attribute CTG, with 0.08\% training parameters of a GPT-2.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2204.13362 [cs.CL]
  (or arXiv:2204.13362v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2204.13362

Submission history

From: Kexin Yang [view email]
[v1] Thu, 28 Apr 2022 09:09:45 UTC (2,959 KB)
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