A new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable.
Source: HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
Variants: HybridQA
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Question Answering | MATE Ponter | MATE: Multi-view Attention for Table … | 2021-09-09 |
| Question Answering | DocHopper | Iterative Hierarchical Attention for Answering … | 2021-06-01 |
| Question Answering | HYBRIDER | HybridQA: A Dataset of Multi-Hop … | 2020-04-15 |
Recent papers with results on this dataset: