Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog
Kewei Cheng, Xian Li, Zhengyang Wang, Chenwei Zhang, Binxuan Huang, Yifan Ethan Xu, Xin Luna Dong, Yizhou Sun
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\% on PR AUC over attribute applicability classification task and by 11\% on PR AUC over attribute value validation task.
BibTeX
@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"
}