CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese

Go Inoue, Akihiko Kato, Masato Mita, Ukyo Honda, Peinan Zhang


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 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.
Anthology ID:
2024.lrec-main.242
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
2702–2707
Language:
URL:
https://aclanthology.org/2024.lrec-main.242
DOI:
Bibkey:
Cite (ACL):
Go Inoue, Akihiko Kato, Masato Mita, Ukyo Honda, and Peinan Zhang. 2024. CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2702–2707, Torino, Italia. ELRA and ICCL.
Cite (Informal):
CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese (Inoue et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.lrec-main.242.pdf