@inproceedings{inoue-etal-2024-camera3-evaluation,
title = "{CAMERA}{\mbox{$^3$}}: An Evaluation Dataset for Controllable Ad Text Generation in {J}apanese",
author = "Inoue, Go and
Kato, Akihiko and
Mita, Masato and
Honda, Ukyo and
Zhang, Peinan",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.242",
pages = "2702--2707",
abstract = "Ad text generation is the task of creating compelling text from an advertising asset that describes products or services, such as a landing page. In advertising, diversity plays an important role in enhancing the effectiveness of an ad text, mitigating a phenomenon called {``}ad fatigue,{''} where users become disengaged due to repetitive exposure to the same advertisement. Despite numerous efforts in ad text generation, the aspect of diversifying ad texts has received limited attention, particularly in non-English languages like Japanese. To address this, we present CAMERA{\mbox{$^3$}}, an evaluation dataset for controllable text generation in the advertising domain in Japanese. Our dataset includes 3,980 ad texts written by expert annotators, taking into account various aspects of ad appeals. We make CAMERA{\mbox{$^3$}} publicly available, allowing researchers to examine the capabilities of recent NLG models in controllable text generation in a real-world scenario.",
}
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%0 Conference Proceedings
%T CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese
%A Inoue, Go
%A Kato, Akihiko
%A Mita, Masato
%A Honda, Ukyo
%A Zhang, Peinan
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F inoue-etal-2024-camera3-evaluation
%X Ad text generation is the task of creating compelling text from an advertising asset that describes products or services, such as a landing page. In advertising, diversity plays an important role in enhancing the effectiveness of an ad text, mitigating a phenomenon called “ad fatigue,” where users become disengaged due to repetitive exposure to the same advertisement. Despite numerous efforts in ad text generation, the aspect of diversifying ad texts has received limited attention, particularly in non-English languages like Japanese. To address this, we present CAMERA³, an evaluation dataset for controllable text generation in the advertising domain in Japanese. Our dataset includes 3,980 ad texts written by expert annotators, taking into account various aspects of ad appeals. We make CAMERA³ publicly available, allowing researchers to examine the capabilities of recent NLG models in controllable text generation in a real-world scenario.
%U https://aclanthology.org/2024.lrec-main.242
%P 2702-2707
Markdown (Informal)
[CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese](https://aclanthology.org/2024.lrec-main.242) (Inoue et al., LREC-COLING 2024)
ACL