@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",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning
%A Lei, Zeyang
%A Zhang, Chao
%A Xu, Xinchao
%A Wu, Wenquan
%A Niu, Zheng-yu
%A Wu, Hua
%A Wang, Haifeng
%A Yang, Yi
%A Li, Shuanglong
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F lei-etal-2022-plato
%X 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.
%R 10.18653/v1/2022.emnlp-industry.52
%U https://aclanthology.org/2022.emnlp-industry.52/
%U https://doi.org/10.18653/v1/2022.emnlp-industry.52
%P 512-520
Markdown (Informal)
[PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning](https://aclanthology.org/2022.emnlp-industry.52/) (Lei et al., EMNLP 2022)
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.