@inproceedings{sugiyama-etal-2026-adcompliance,
title = "{A}d{C}ompliance: Towards Automated Compliance Verification in Advertising Operations",
author = "Sugiyama, Saki and
Zhang, Peinan and
Sakai, Yusuke and
Takase, Sho and
Ouchi, Hiroki and
Watanabe, Taro",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.254/",
pages = "4295--4304",
ISBN = "979-8-89176-460-6",
abstract = "Advertising operations (AdOps) include compliance review processes to ensure that advertisements adhere to applicable laws, platform policies, and industry guidelines. In particular, ads must avoid issues such as overclaiming effectiveness or making definitive assertions about uncertain outcomes. However, these reviews are typically conducted by experts, making them an operational bottleneck. In this paper, we propose the automated ad text compliance verification task and present our classifier-based system. We introduce AdCompliance, a dataset consisting of real pre-submission ad texts annotated by experts. Our experimental results demonstrate practical classification performance, suggesting that non-compliant ads can be effectively filtered while maintaining a low false positive rate. These results support practical integration into AdOps workflows."
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<abstract>Advertising operations (AdOps) include compliance review processes to ensure that advertisements adhere to applicable laws, platform policies, and industry guidelines. In particular, ads must avoid issues such as overclaiming effectiveness or making definitive assertions about uncertain outcomes. However, these reviews are typically conducted by experts, making them an operational bottleneck. In this paper, we propose the automated ad text compliance verification task and present our classifier-based system. We introduce AdCompliance, a dataset consisting of real pre-submission ad texts annotated by experts. Our experimental results demonstrate practical classification performance, suggesting that non-compliant ads can be effectively filtered while maintaining a low false positive rate. These results support practical integration into AdOps workflows.</abstract>
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%0 Conference Proceedings
%T AdCompliance: Towards Automated Compliance Verification in Advertising Operations
%A Sugiyama, Saki
%A Zhang, Peinan
%A Sakai, Yusuke
%A Takase, Sho
%A Ouchi, Hiroki
%A Watanabe, Taro
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F sugiyama-etal-2026-adcompliance
%X Advertising operations (AdOps) include compliance review processes to ensure that advertisements adhere to applicable laws, platform policies, and industry guidelines. In particular, ads must avoid issues such as overclaiming effectiveness or making definitive assertions about uncertain outcomes. However, these reviews are typically conducted by experts, making them an operational bottleneck. In this paper, we propose the automated ad text compliance verification task and present our classifier-based system. We introduce AdCompliance, a dataset consisting of real pre-submission ad texts annotated by experts. Our experimental results demonstrate practical classification performance, suggesting that non-compliant ads can be effectively filtered while maintaining a low false positive rate. These results support practical integration into AdOps workflows.
%U https://aclanthology.org/2026.emnlp-industry.254/
%P 4295-4304
Markdown (Informal)
[AdCompliance: Towards Automated Compliance Verification in Advertising Operations](https://aclanthology.org/2026.emnlp-industry.254/) (Sugiyama et al., EMNLP 2026)
ACL