TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

@inproceedings{Yin2020TaBERTPF,
  title={TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data},
  author={Pengcheng Yin and Graham Neubig and Wen-tau Yih and Sebastian Riedel},
  booktitle={ACL},
  year={2020}
}
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… Expand
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
ColloQL: Robust Text-to-SQL Over Search Queries
Retrieving Complex Tables with Multi-Granular Graph Representation Learning
Multimodal AutoML on Tables with Text Fields
  • Xingjian Shi, Jonas Mueller
  • 2021
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 56 REFERENCES
ERNIE: Enhanced Language Representation with Informative Entities
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
Global Reasoning over Database Structures for Text-to-SQL Parsing
TabFact: A Large-scale Dataset for Table-based Fact Verification
Semantic Parsing via Paraphrasing
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
Knowledge Enhanced Contextual Word Representations
Semantic Parsing on Freebase from Question-Answer Pairs
Compositional Semantic Parsing on Semi-Structured Tables
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
...
1
2
3
4
5
...