@inproceedings{shi-etal-2021-keyword,
title = "Keyword Augmentation via Generative Methods",
author = "Shi, Haoran and
Rao, Zhibiao and
Wu, Yongning and
Zhang, Zuohua and
Wang, Chu",
editor = "Malmasi, Shervin and
Kallumadi, Surya and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the 4th Workshop on e-Commerce and NLP",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.ecnlp-1.5",
doi = "10.18653/v1/2021.ecnlp-1.5",
pages = "33--37",
abstract = "Keyword augmentation is a fundamental problem for sponsored search modeling and business. Machine generated keywords can be recommended to advertisers for better campaign discoverability as well as used as features for sourcing and ranking models. Generating high-quality keywords is difficult, especially for cold campaigns with limited or even no historical logs; and the industry trend of including multiple products in a single ad campaign is making the problem more challenging. In this paper, we propose a keyword augmentation method based on generative seq2seq model and trie-based search mechanism, which is able to generate high-quality keywords for any products or product lists. We conduct human annotations, offline analysis, and online experiments to evaluate the performance of our method against benchmarks in terms of augmented keyword quality as well as lifted ad exposure. The experiment results demonstrate that our method is able to generate more valid keywords which can serve as an efficient addition to advertiser selected keywords.",
}
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<abstract>Keyword augmentation is a fundamental problem for sponsored search modeling and business. Machine generated keywords can be recommended to advertisers for better campaign discoverability as well as used as features for sourcing and ranking models. Generating high-quality keywords is difficult, especially for cold campaigns with limited or even no historical logs; and the industry trend of including multiple products in a single ad campaign is making the problem more challenging. In this paper, we propose a keyword augmentation method based on generative seq2seq model and trie-based search mechanism, which is able to generate high-quality keywords for any products or product lists. We conduct human annotations, offline analysis, and online experiments to evaluate the performance of our method against benchmarks in terms of augmented keyword quality as well as lifted ad exposure. The experiment results demonstrate that our method is able to generate more valid keywords which can serve as an efficient addition to advertiser selected keywords.</abstract>
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%0 Conference Proceedings
%T Keyword Augmentation via Generative Methods
%A Shi, Haoran
%A Rao, Zhibiao
%A Wu, Yongning
%A Zhang, Zuohua
%A Wang, Chu
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 4th Workshop on e-Commerce and NLP
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F shi-etal-2021-keyword
%X Keyword augmentation is a fundamental problem for sponsored search modeling and business. Machine generated keywords can be recommended to advertisers for better campaign discoverability as well as used as features for sourcing and ranking models. Generating high-quality keywords is difficult, especially for cold campaigns with limited or even no historical logs; and the industry trend of including multiple products in a single ad campaign is making the problem more challenging. In this paper, we propose a keyword augmentation method based on generative seq2seq model and trie-based search mechanism, which is able to generate high-quality keywords for any products or product lists. We conduct human annotations, offline analysis, and online experiments to evaluate the performance of our method against benchmarks in terms of augmented keyword quality as well as lifted ad exposure. The experiment results demonstrate that our method is able to generate more valid keywords which can serve as an efficient addition to advertiser selected keywords.
%R 10.18653/v1/2021.ecnlp-1.5
%U https://aclanthology.org/2021.ecnlp-1.5
%U https://doi.org/10.18653/v1/2021.ecnlp-1.5
%P 33-37
Markdown (Informal)
[Keyword Augmentation via Generative Methods](https://aclanthology.org/2021.ecnlp-1.5) (Shi et al., ECNLP 2021)
ACL
- Haoran Shi, Zhibiao Rao, Yongning Wu, Zuohua Zhang, and Chu Wang. 2021. Keyword Augmentation via Generative Methods. In Proceedings of the 4th Workshop on e-Commerce and NLP, pages 33–37, Online. Association for Computational Linguistics.