@inproceedings{zhao-etal-2022-self,
title = "Self-supervised Product Title Rewrite for Product Listing Ads",
author = "Zhao, Xue and
Liu, Dayiheng and
Ding, Junwei and
Yao, Liang and
Yan, Mahone and
Wang, Huibo and
Yao, Wenqing",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.10",
doi = "10.18653/v1/2022.naacl-industry.10",
pages = "79--85",
abstract = "Product Listing Ads (PLAs) are primary online advertisements merchants pay to attract more customers. However, merchants prefer to stack various attributes to the title and neglect the fluency and information priority. These seller-created titles are not suitable for PLAs as they fail to highlight the core information in the visible part in PLAs titles. In this work, we present a title rewrite solution. Specifically, we train a self-supervised language model to generate high-quality titles in terms of fluency and information priority. Extensive offline test and real-world online test have demonstrated that our solution is effective in reducing the cost and gaining more profit as it lowers our CPC, CPB while improving CTR in the online test by a large amount.",
}
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<abstract>Product Listing Ads (PLAs) are primary online advertisements merchants pay to attract more customers. However, merchants prefer to stack various attributes to the title and neglect the fluency and information priority. These seller-created titles are not suitable for PLAs as they fail to highlight the core information in the visible part in PLAs titles. In this work, we present a title rewrite solution. Specifically, we train a self-supervised language model to generate high-quality titles in terms of fluency and information priority. Extensive offline test and real-world online test have demonstrated that our solution is effective in reducing the cost and gaining more profit as it lowers our CPC, CPB while improving CTR in the online test by a large amount.</abstract>
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%0 Conference Proceedings
%T Self-supervised Product Title Rewrite for Product Listing Ads
%A Zhao, Xue
%A Liu, Dayiheng
%A Ding, Junwei
%A Yao, Liang
%A Yan, Mahone
%A Wang, Huibo
%A Yao, Wenqing
%Y Loukina, Anastassia
%Y Gangadharaiah, Rashmi
%Y Min, Bonan
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F zhao-etal-2022-self
%X Product Listing Ads (PLAs) are primary online advertisements merchants pay to attract more customers. However, merchants prefer to stack various attributes to the title and neglect the fluency and information priority. These seller-created titles are not suitable for PLAs as they fail to highlight the core information in the visible part in PLAs titles. In this work, we present a title rewrite solution. Specifically, we train a self-supervised language model to generate high-quality titles in terms of fluency and information priority. Extensive offline test and real-world online test have demonstrated that our solution is effective in reducing the cost and gaining more profit as it lowers our CPC, CPB while improving CTR in the online test by a large amount.
%R 10.18653/v1/2022.naacl-industry.10
%U https://aclanthology.org/2022.naacl-industry.10
%U https://doi.org/10.18653/v1/2022.naacl-industry.10
%P 79-85
Markdown (Informal)
[Self-supervised Product Title Rewrite for Product Listing Ads](https://aclanthology.org/2022.naacl-industry.10) (Zhao et al., NAACL 2022)
ACL
- Xue Zhao, Dayiheng Liu, Junwei Ding, Liang Yao, Mahone Yan, Huibo Wang, and Wenqing Yao. 2022. Self-supervised Product Title Rewrite for Product Listing Ads. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pages 79–85, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.