Table Question Answering Models
TAPAS
Introduced by Herzig et al. in TAPAS: Weakly Supervised Table Parsing via Pre-trainingTAPAS is a weakly supervised question answering model that reasons over tables without generating logical forms. TAPAS predicts a minimal program by selecting a subset of the table cells and a possible aggregation operation to be executed on top of them. Consequently, TAPAS can learn operations from natural language, without the need to specify them in some formalism. This is implemented by extending BERT’s architecture with additional embeddings that capture tabular structure, and with two classification layers for selecting cells and predicting a corresponding aggregation operator.
Source: TAPAS: Weakly Supervised Table Parsing via Pre-training
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Question Answering | 2 | 14.29% |
| Natural Language Inference | 2 | 14.29% |
| Table-based Fact Verification | 2 | 14.29% |
| Decision Making | 1 | 7.14% |
| Data-to-Text Generation | 1 | 7.14% |
| Table-to-Text Generation | 1 | 7.14% |
| Text Generation | 1 | 7.14% |
| Fact Verification | 1 | 7.14% |
| Logical Reasoning | 1 | 7.14% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
