An Empirical Study of Generating Texts for Search Engine Advertising

Hidetaka Kamigaito, Peinan Zhang, Hiroya Takamura, Manabu Okumura


Abstract
Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain. Generating ads with NLG models can help copywriters in their creation. However, few studies have adequately evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. In this paper, we demonstrate a practical use case of generating ad-text with an NLG model. Specially, we show how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads.
Anthology ID:
2021.naacl-industry.32
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
255–262
Language:
URL:
https://aclanthology.org/2021.naacl-industry.32
DOI:
10.18653/v1/2021.naacl-industry.32
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-industry.32.pdf