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
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.
BibTeX
@inproceedings{lei-etal-2022-plato,
title = "{PLATO}-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning",
author = "Lei, Zeyang and
Zhang, Chao and
Xu, Xinchao and
Wu, Wenquan and
Niu, Zheng-yu and
Wu, Hua and
Wang, Haifeng and
Yang, Yi and
Li, Shuanglong",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.52/",
doi = "10.18653/v1/2022.emnlp-industry.52",
pages = "512--520"
}