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        <title>An Empirical Study of Generating Texts for Search Engine Advertising</title>
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        <namePart type="given">Hidetaka</namePart>
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    <name type="personal">
        <namePart type="given">Manabu</namePart>
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            <title>Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T An Empirical Study of Generating Texts for Search Engine Advertising
%A Kamigaito, Hidetaka
%A Zhang, Peinan
%A Takamura, Hiroya
%A Okumura, Manabu
%Y Kim, Young-bum
%Y Li, Yunyao
%Y Rambow, Owen
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F kamigaito-etal-2021-empirical
%X 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.
%R 10.18653/v1/2021.naacl-industry.32
%U https://aclanthology.org/2021.naacl-industry.32
%U https://doi.org/10.18653/v1/2021.naacl-industry.32
%P 255-262

Markdown (Informal)

[An Empirical Study of Generating Texts for Search Engine Advertising](https://aclanthology.org/2021.naacl-industry.32) (Kamigaito et al., NAACL 2021)

• An Empirical Study of Generating Texts for Search Engine Advertising (Kamigaito et al., NAACL 2021)

ACL

• Hidetaka Kamigaito, Peinan Zhang, Hiroya Takamura, and Manabu Okumura. 2021. An Empirical Study of Generating Texts for Search Engine Advertising. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 255–262, Online. Association for Computational Linguistics.