Papers › Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation

Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation

12 Dec 2023Proceedings of the ACM on Management of Data 2023 12archive 2025-07-28

Zhengjie Miao, Jin Wang

Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the field of data management. Despite recent efforts to improve different tasks in table understanding by using the power of large pre-trained language models, existing methods heavily rely on large-scale and high-quality labeled instances, while they still suffer from the data sparsity problem due to the imbalanced data distribution among different classes. In this paper, we propose the Watchog framework, which employs contrastive learning techniques to learn robust representations for tables by leveraging a large-scale unlabeled table corpus with minimal overhead. Our approach enables the learned table representations to enhance fine tuning with much fewer additional labeled instances than in prior studies for downstream column annotation tasks. Besides, we further proposed optimization techniques for semi-supervised settings. Experimental results on popular benchmarking datasets illustrate the superiority of our proposed techniques in two column annotation tasks under different settings. In particular, our Watchog framework effectively alleviates the class imbalance issue caused by a long-tailed label distribution. In the semi-supervised setting, Watchog outperforms the best-known method by up to 26% and 41% in Micro and Macro F1 scores, respectively, on the task of semantic type detection.

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Tasks

BenchmarkingColumn Type AnnotationColumns Property AnnotationContrastive Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Column Type Annotation VizNet-Sato-Full Watchog Macro-F1 85.63 #1 of 4 Archive leaderboard report
Column Type Annotation WikiTables-TURL-CTA Watchog Macro-F1 78.72 #3 of 3 Archive leaderboard report
Columns Property Annotation WikiTables-TURL-CPA Watchog Macro-F1 88.45 #3 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Contrastive Learning

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