PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning

Zeyang Lei, Chao Zhang, Xinchao Xu, Wenquan Wu, Zheng-yu Niu, Hua Wu, Haifeng Wang, Yi Yang, Shuanglong Li


Abstract
Online advertisement text generation aims at generating attractive and persuasive text ads to appeal to users clicking ads or purchasing products. While pretraining-based models have achieved remarkable success in generating high-quality text ads, some challenges still remain, such as ad generation in low-resource scenarios and training efficiency for multiple ad tasks. In this paper, we propose a novel unified text ad generation framework with multi-task prompt learning, called PLATO-Ad, totackle these problems. Specifically, we design a three-phase transfer learning mechanism to tackle the low-resource ad generation problem. Furthermore, we present a novel multi-task prompt learning mechanism to efficiently utilize a single lightweight model to solve multiple ad generation tasks without loss of performance compared to training a separate model for each task. Finally, we conduct offline and online evaluations and experiment results show that PLATO-Ad significantly outperforms the state-of-the-art on both offline and online metrics. PLATO-Ad has been deployed in a leading advertising platform with 3.5% CTR improvement on search ad descriptions and 10.4% CTR improvement on feed ad titles.
Anthology ID:
2022.emnlp-industry.52
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Editors:
Yunyao Li, Angeliki Lazaridou
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
512–520
Language:
URL:
https://aclanthology.org/2022.emnlp-industry.52
DOI:
10.18653/v1/2022.emnlp-industry.52
Bibkey:
Cite (ACL):
Zeyang Lei, Chao Zhang, Xinchao Xu, Wenquan Wu, Zheng-yu Niu, Hua Wu, Haifeng Wang, Yi Yang, and Shuanglong Li. 2022. PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 512–520, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning (Lei et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-industry.52.pdf