@inproceedings{cheng-etal-2023-tab,
    title = "Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for {E}-commerce Catalog",
    author = "Cheng, Kewei  and
      Li, Xian  and
      Wang, Zhengyang  and
      Zhang, Chenwei  and
      Huang, Binxuan  and
      Xu, Yifan Ethan  and
      Dong, Xin Luna  and
      Sun, Yizhou",
    editor = "Sitaram, Sunayana  and
      Beigman Klebanov, Beata  and
      Williams, Jason D",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-industry.18",
    doi = "10.18653/v1/2023.acl-industry.18",
    pages = "172--185",
    abstract = "Product catalogs, conceptually in the form of text-rich tables, are self-reported by individual retailers and thus inevitably contain noisy facts. Verifying such textual attributes in product catalogs is essential to improve their reliability. However, popular methods for processing free-text content, such as pre-trained language models, are not particularly effective on structured tabular data since they are typically trained on free-form natural language texts. In this paper, we present Tab-Cleaner, a model designed to handle error detection over text-rich tabular data following a pre-training / fine-tuning paradigm. We train Tab-Cleaner on a real-world Amazon Product Catalog table w.r.t millions of products and show improvements over state-of-the-art methods by 16{\textbackslash}{\%} on PR AUC over attribute applicability classification task and by 11{\textbackslash}{\%} on PR AUC over attribute value validation task.",
}

Product catalogs, conceptually in the form of text-rich tables, are self-reported by individual retailers and thus inevitably contain noisy facts. Verifying such textual attributes in product catalogs is essential to improve their reliability. However, popular methods for processing free-text content, such as pre-trained language models, are not particularly effective on structured tabular data since they are typically trained on free-form natural language texts. In this paper, we present Tab-Cleaner, a model designed to handle error detection over text-rich tabular data following a pre-training / fine-tuning paradigm. We train Tab-Cleaner on a real-world Amazon Product Catalog table w.r.t millions of products and show improvements over state-of-the-art methods by 16{\textbackslash}{\%} on PR AUC over attribute applicability classification task and by 11{\textbackslash}{\%} on PR AUC over attribute value validation task.
Cheng, Kewei Li, Xian Wang, Zhengyang Zhang, Chenwei Huang, Binxuan Xu, Yifan Ethan Dong, Xin Luna Sun, Yizhou
Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog