Papers › TURL: Table Understanding through Representation Learning

TURL: Table Understanding through Representation Learning

26 Jun 2020arXiv:2006.14806archive 2025-07-28

Xiang Deng, Huan Sun, Alyssa Lees, You Wu, Cong Yu

Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table understanding. However, existing work generally relies on heavily-engineered task-specific features and model architectures. In this paper, we present TURL, a novel framework that introduces the pre-training/fine-tuning paradigm to relational Web tables. During pre-training, our framework learns deep contextualized representations on relational tables in an unsupervised manner. Its universal model design with pre-trained representations can be applied to a wide range of tasks with minimal task-specific fine-tuning. Specifically, we propose a structure-aware Transformer encoder to model the row-column structure of relational tables, and present a new Masked Entity Recovery (MER) objective for pre-training to capture the semantics and knowledge in large-scale unlabeled data. We systematically evaluate TURL with a benchmark consisting of 6 different tasks for table understanding (e.g., relation extraction, cell filling). We show that TURL generalizes well to all tasks and substantially outperforms existing methods in almost all instances.

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Tasks

Cell Entity AnnotationColumn Type AnnotationColumns Property AnnotationRelation ExtractionRepresentation LearningTable annotation

Datasets

Introduced by this paper, per the archive.

WikiTables-TURL

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Cell Entity Annotation WikiTables-TURL-CEA TURL F1 (%) 68 #1 of 1 Archive leaderboard report
Cell Entity Annotation WikipediaGS TURL F1 (%) 67 #1 of 1 Archive leaderboard report
Column Type Annotation T2Dv2 TURL Accuracy (%) 96.2 #2 of 3 Archive leaderboard report
Column Type Annotation WikiTables-TURL-CTA TURL F1 (%) 94.75 #1 of 3 Archive leaderboard report
Column Type Annotation WikipediaGS-CTA TURL Accuracy (%) 74.6 #1 of 2 Archive leaderboard report
Columns Property Annotation WikiTables-TURL-CPA TURL F1 (%) 94.91 #1 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

Introduced by this paper: TURL

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerReLUResidual ConnectionSoftmaxTURLTransformer

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