@inproceedings{zhang-etal-2025-adtec,
title = "{A}d{TEC}: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising",
author = "Zhang, Peinan and
Sakai, Yusuke and
Mita, Masato and
Ouchi, Hiroki and
Watanabe, Taro",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.391/",
pages = "7672--7691",
ISBN = "979-8-89176-189-6",
abstract = "As the fluency of ad texts automatically generated by natural language generation technologies continues to improve, there is an increasing demand to assess the quality of these creatives in real-world setting.We propose **AdTEC**, the first public benchmark to evaluate ad texts from multiple perspectives within practical advertising operations.Our contributions are as follows: (i) Defining five tasks for evaluating the quality of ad texts, as well as constructing a Japanese dataset based on the practical operational experiences of advertising agencies, which are typically maintained in-house. (ii) Validating the performance of existing pre-trained language models (PLMs) and human evaluators on this dataset. (iii) Analyzing the characteristics and providing challenges of the benchmark.Our results show that while PLMs have a practical level of performance in several tasks, humans continue to outperform them in certain domains, indicating that there remains significant potential for further improvement in this area."
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<abstract>As the fluency of ad texts automatically generated by natural language generation technologies continues to improve, there is an increasing demand to assess the quality of these creatives in real-world setting.We propose **AdTEC**, the first public benchmark to evaluate ad texts from multiple perspectives within practical advertising operations.Our contributions are as follows: (i) Defining five tasks for evaluating the quality of ad texts, as well as constructing a Japanese dataset based on the practical operational experiences of advertising agencies, which are typically maintained in-house. (ii) Validating the performance of existing pre-trained language models (PLMs) and human evaluators on this dataset. (iii) Analyzing the characteristics and providing challenges of the benchmark.Our results show that while PLMs have a practical level of performance in several tasks, humans continue to outperform them in certain domains, indicating that there remains significant potential for further improvement in this area.</abstract>
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%0 Conference Proceedings
%T AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising
%A Zhang, Peinan
%A Sakai, Yusuke
%A Mita, Masato
%A Ouchi, Hiroki
%A Watanabe, Taro
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F zhang-etal-2025-adtec
%X As the fluency of ad texts automatically generated by natural language generation technologies continues to improve, there is an increasing demand to assess the quality of these creatives in real-world setting.We propose **AdTEC**, the first public benchmark to evaluate ad texts from multiple perspectives within practical advertising operations.Our contributions are as follows: (i) Defining five tasks for evaluating the quality of ad texts, as well as constructing a Japanese dataset based on the practical operational experiences of advertising agencies, which are typically maintained in-house. (ii) Validating the performance of existing pre-trained language models (PLMs) and human evaluators on this dataset. (iii) Analyzing the characteristics and providing challenges of the benchmark.Our results show that while PLMs have a practical level of performance in several tasks, humans continue to outperform them in certain domains, indicating that there remains significant potential for further improvement in this area.
%U https://aclanthology.org/2025.naacl-long.391/
%P 7672-7691
Markdown (Informal)
[AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising](https://aclanthology.org/2025.naacl-long.391/) (Zhang et al., NAACL 2025)
ACL
- Peinan Zhang, Yusuke Sakai, Masato Mita, Hiroki Ouchi, and Taro Watanabe. 2025. AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7672–7691, Albuquerque, New Mexico. Association for Computational Linguistics.