Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
Japanese
Size:
10K - 100K
ArXiv:
License:
| license: cc-by-nc-sa-4.0 | |
| task_categories: | |
| - text-classification | |
| language: | |
| - ja | |
| multilinguality: | |
| - monolingual | |
| source_datasets: | |
| - original | |
| pretty_name: adtec | |
| size_categories: | |
| - 100K<n<1M | |
| tags: | |
| - ads | |
| - advertisement | |
| - adnlp | |
| configs: | |
| - config_name: ad-acceptability | |
| default: true | |
| data_files: | |
| - split: train | |
| path: "data/ad-acceptability/train.tsv" | |
| - split: valid | |
| path: "data/ad-acceptability/valid.tsv" | |
| - split: test | |
| path: "data/ad-acceptability/test.tsv" | |
| - config_name: ad-consistency | |
| data_files: | |
| - split: train | |
| path: "data/ad-consistency/train.tsv" | |
| - split: valid | |
| path: "data/ad-consistency/valid.tsv" | |
| - split: test | |
| path: "data/ad-consistency/test.tsv" | |
| - config_name: ad-similarity | |
| data_files: | |
| - split: train | |
| path: "data/ad-similarity/train.tsv" | |
| - split: valid | |
| path: "data/ad-similarity/valid.tsv" | |
| - split: test | |
| path: "data/ad-similarity/test.tsv" | |
| - config_name: a3-recognition | |
| data_files: | |
| - split: train | |
| path: "data/a3-recognition/train.tsv" | |
| - split: valid | |
| path: "data/a3-recognition/valid.tsv" | |
| - split: test | |
| path: "data/a3-recognition/test.tsv" | |
| # Dataset Card for AdTEC | |
| [](https://arxiv.org/abs/2408.05906) | |
| [](https://github.com/cyberagentailab/adtec) | |
| [](https://cyberagentailab.github.io/AdTEC/) | |
| [](https://github.com/CyberAgentAILab/AdTEC/blob/main/materials/NAACL2025-poster.pdf) | |
| [](https://github.com/CyberAgentAILab/AdTEC/blob/main/materials/NAACL2025-slides.pdf) | |
| [](https://youtu.be/3QmKidnlkiI) | |
| ## Dataset Description | |
| - **Homepage:** [Project Page](https://cyberagentailab.github.io/AdTEC/) | |
| - **Repository:** [GitHub](https://github.com/cyberagentailab/adtec/) | |
| - **Paper:** [AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising](https://arxiv.org/abs/2408.05906) | |
| - **Leaderboard:** | |
| - **Point of Contact:** [Peinan Zhang](https://github.com/peinan) | |
| The AdTEC dataset is designed to evaluate the quality of ad texts from multiple aspects, considering practical advertising operations. | |
| ## Experiments and Tasks Considered in the Paper | |
| This dataset includes five tasks: | |
| - **Ad Acceptability**: Given a text, predict the acceptance of overall quality with binary labels: `acceptable`/`unacceptable`. | |
| - **Ad Consistency**: Given a pair of ad text and landing page (LP) text, predict the consistency between the ad and LP text with binary labels: `consistent`/`inconsistent`. | |
| - **Ad Performance Estimation**: Given ad texts, keywords, and industry type, predict the overall performance with a score ranging from 0 to 100. | |
| - **A3 Recognition**: Given a text, predict all possible aspects of advertising appeals (A3). The comprehensive list of A3 can be found in [[Murakami et al., 2022](https://aclanthology.org/2022.naacl-industry.9/)]. | |
| - **Ad Similarity**: Given a pair of ad texts, predict their similarity with a score ranging from 1 to 5. | |
| ## Languages | |
| The dataset contains Japanese text only. | |
| ## Domain | |
| Online advertisement (Search engine advertisement). | |
| ## Dataset Curators | |
| The dataset was created by Peinan Zhang, Yusuke Sakai, Masato Mita, Hiroki Ouchi, and Taro Watanabe. | |
| ## Dataset Structure | |
| All tasks are defined in TSV format and split into train, dev, and test sets. | |
| The number of instances for each task in each split is shown in the table below. | |
| | Task | Train | Dev | Test | | |
| |------|------:|----:|-----:| | |
| | Ad Acceptability | 13,265 | 970 | 980 | | |
| | Ad Consistency | 10,635 | 945 | 970 | | |
| | Ad Performance Estimation | 125,087 | 965 | 965 | | |
| | A3 Recognition | 1,856 | 465 | 410 | | |
| | Ad Similarity | 4,980 | 623 | 629 | | |
| Detailed examples of each task are provided below. | |
| ### Ad Acceptability | |
| ```tsv | |
| label title | |
| acceptable 最新のビデオキャプチャー年間ランキングをご紹介・会員ランクに応じてポイント獲得がお得に。 | |
| unacceptable 茨城県アパート以上 | |
| acceptable 気になる保障をカンタン見積り | |
| ... | |
| ``` | |
| ### Ad Consistency | |
| ```tsv | |
| label lp_text title | |
| consistent 介護の転職サポート登録のページ。介護の求人・転職なら介護ワーカー。介護福祉士、ホームヘルパー、ケアマネ、社会福祉士などの求人情報がどこよりも詳しくわかる！あなたの理想の職場がきっと見つかる！ 介護職の求人なら【名古屋市】 | |
| inconsistent 手ぶらで体験レッスンが0円！上下ウェア・バスタオル・フェイスタオル・ヨガマット無料レンタルだから、手ぶらでOK！さらにお水（550ml×2本）をプレゼント！ ヨガ大分スタジオ「ロイブ」 | |
| consistent ホーム コース ゲーム総合科 ゲーム総合科 プロの講師陣があなたのなりゲームクリエイターになるにはを全力サポート！資料請求はこちらから | |
| ... | |
| ``` | |
| ### Ad Performance Estimation | |
| ```tsv | |
| industry_type keyword title_1 title_2 title_3 description_1 description_2 score | |
| EC hunter 【公式】[MASK_2]（[MASK_8]） 《MAX50% OFF》AUTUMN SALE 人気＆注目アイテムはこちらから 人気商品がMAX50%OFF！機能性抜群の[MASK_8]アイテムで秋を楽しく過ごそう 機能的でスタイリッシュなデザイン。創業当初から愛される「機能美」をあなたへ。 55.90851442 | |
| 金融 かしつけ 急ぎでお金の貸付受けたい方必見 免許証だけで最短1時間貸付可能 ≪最短1h融資可能≫ローン貸付ランキング24hスマホ完結申込。融資可能か1秒診断 79.01220606 | |
| ... | |
| ``` | |
| ### A3 Recognition | |
| ```tsv | |
| title labels | |
| THE THOUSAND KYOTO（ザ・サウザンド京都）の最寄駅は京都駅。THE THOUSAND KYOTO。ブライダルフェア情報、おトクな割引特典、フォトギャラリー、口コミ、見積もり例が充実！結婚式場探しはハナユメ！ 商品/サービス特徴|オファー|アクセス | |
| 鳥取ガス enetopia | |
| ジャニーズ屈指のダンス力を誇る7人組グループ「Travis Japan」を全4週にわたって特集！ 商品/サービス特徴 | |
| ... | |
| ``` | |
| ### Ad Similarity | |
| ```tsv | |
| text1 text2 score | |
| ３月３１日まで実施中 山口トヨタ／お客様大感謝祭 2.67 | |
| 春得キャンペーン第1弾 春得キャンペーン 4.33 | |
| ... | |
| ``` | |
| ## License | |
| AdTEC dataset is released under the [CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International license](./LICENSE). | |
| ## Citation | |
| ```latex | |
| @inproceedings{zhang2025adtec, | |
| title={{AdTEC}: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising}, | |
| author={Peinan Zhang and Yusuke Sakai and Masato Mita and Hiroki Ouchi and Taro Watanabe}, | |
| booktitle={Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL)}, | |
| year={2025}, | |
| publisher={Association for Computational Linguistics}, | |
| eprint={2408.05906}, | |
| primaryClass={cs.CL}, | |
| url={https://arxiv.org/abs/2408.05906}, | |
| } | |
| ``` | |