Papers › TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, Sebastian Riedel
Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables). In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider. Implementation of the model will be available at http://fburl.com/TaBERT .
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Parsing | WikiTableQuestions | MAPO + TABERTLarge (K = 3) | Accuracy (Dev) | 52.2 | #18 of 22 | Archive leaderboard | report |
| Semantic Parsing | WikiTableQuestions | MAPO + TABERTLarge (K = 3) | Accuracy (Test) | 51.8 | #18 of 22 | Archive leaderboard | report |
| Text-To-SQL | spider | MAPO + TABERTLarge (K = 3) | Exact Match Accuracy (Dev) | 64.5 | #20 of 20 | 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: TaBERT
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