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TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
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Recent years have witnessed the burgeoning of pretrained language models (LMs) for textbased 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. 1
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“Transformer with global receptive field could effectively learn global representation and longrange dependency without absolute position information, achieving excellent performance on nonsequential data (i.e., images, tables) 24,27 .”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [27]
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Lù, X. H., Reddy, S., & de Vries, H. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.206 · Cited by 6
Pan, F., Canim, M., Glass, M., Gliozzo, A., & Fox, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04441 · Cited by 4
TVRecap: A Dataset for Generating Stories with Character Descriptions.Preprint
Chen, M., & Gimpel, K. (2021) · arXiv (Cornell University) · Cited by 4
Open Domain Question Answering over Virtual Documents: A Unified Approach for Data and Text.Preprint
Ma, K., Cheng, H., Liu, X., Nyberg, E., & Gao, J. (2021) · arXiv (Cornell University) · Cited by 4
Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective
Li, D., Bi, K., Guo, J., & Cheng, X. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1409 · Cited by 7
HierFinRAG—Hierarchical Multimodal RAG for Financial Document Understanding
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Metadata
- Authors
- Yin, P., Neubig, G., Yih, W.-T., & Riedel, S.
- Affiliations
- Meta (United States) (US)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 8413-8426
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3035231859
- Cited by
- 487
- References
- 58
- Citations by year
- 2026: 68 · 2025: 86 · 2024: 72 · 2023: 89 · 2022: 76 · 2021: 84 · 2020: 12
- FWCI
- 24.93
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- pre-trained language models, tabular data, semantic parsing, structured data, natural language understanding, text-to-SQL, spiders, database tables
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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