task_categories:question-answering annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:cc-by-4.0 multihop-tabular-text-qa arxiv:1909.05358 region:us
Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present HybridQA, 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.
@article{chen2020hybridqa,
  title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
  author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
  journal={Findings of EMNLP 2020},
  year={2020}
}
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: hybridqa
pretty_name: HybridQA
tags:
- multihop-tabular-text-qa
dataset_info:
  features:
  - name: question_id
    dtype: string
  - name: question
    dtype: string
  - name: table_id
    dtype: string
  - name: answer_text
    dtype: string
  - name: question_postag
    dtype: string
  - name: table
    struct:
    - name: url
      dtype: string
    - name: title
      dtype: string
    - name: header
      sequence: string
    - name: data
      list:
      - name: value
        dtype: string
      - name: urls
        list:
        - name: url
          dtype: string
        - name: summary
          dtype: string
    - name: section_title
      dtype: string
    - name: section_text
      dtype: string
    - name: uid
      dtype: string
    - name: intro
      dtype: string
  config_name: hybrid_qa
  splits:
  - name: train
    num_bytes: 2745712769
    num_examples: 62682
  - name: validation
    num_bytes: 153512016
    num_examples: 3466
  - name: test
    num_bytes: 148795919
    num_examples: 3463
  download_size: 217436855
  dataset_size: 3048020704
---

# Dataset Card for HybridQA

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://hybridqa.github.io/index.html
- **Repository:** [GitHub](https://github.com/wenhuchen/HybridQA)
- **Paper:** [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** [HybridQA Competition](https://competitions.codalab.org/competitions/24420)
- **Point of Contact:** [Wenhu Chen](wenhuchen@cs.ucsb.edu)


### Dataset Summary

Existing question answering datasets focus on dealing with homogeneous information, based either only on text or
KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms,
using homogeneous information alone might lead to severe coverage problems.
To fill in the gap, we present HybridQA, 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.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

The dataset is in English language.

## Dataset Structure

### Data Instances

A typical example looks like this

```
{
    "question_id": "00009b9649d0dd0a",
    "question": "Who were the builders of the mosque in Herat with fire temples ?",
    "table_id": "List_of_mosques_in_Afghanistan_0",
    "answer_text": "Ghurids",
    "question_postag": "WP VBD DT NNS IN DT NN IN NNP IN NN NNS .",
    "table": {
        "url": "https://en.wikipedia.org/wiki/List_of_mosques_in_Afghanistan",
        "title": "List of mosques in Afghanistan",
        "header": [
            "Name",
            "Province",
            "City",
            "Year",
            "Remarks"
        ],
        "data": [
            {
                "value": "Kabul",
                "urls": [
                    {
                        "summary": "Kabul ( Persian : کابل , romanized : Kābol , Pashto : کابل , romanized : Kābəl ) is the capital and largest city of Afghanistan...",
                        "url": "/wiki/Kabul"
                    }
                ]
            }
        ]
    },
    "section_title": "",
    "section_text": "",
    "uid": "List_of_mosques_in_Afghanistan_0",
    "intro": "The following is an incomplete list of large mosques in Afghanistan:"
}
```

### Data Fields

- `question_id` (str)
- `question` (str)
- `table_id` (str)
- `answer_text` (str)
- `question_postag` (str)
- `table` (dict):
  - `url` (str)
  - `title` (str)
  - `header` (list of str)
  - `data` (list of dict):
    - `value` (str)
    - `urls` (list of dict):
      - `url` (str)
      - `summary` (str)
- `section_title` (str)
- `section_text` (str)
- `uid` (str)
- `intro` (str)

### Data Splits

The dataset is split into `train`, `dev` and `test` splits.

|                 | train | validation | test |
| --------------- |------:|-----------:|-----:|
| N. Instances    | 62682 |       3466 | 3463 |


## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

[More Information Needed]

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

[More Information Needed]

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

The dataset is under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).

### Citation Information

[More Information Needed]
```
@article{chen2020hybridqa,
  title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
  author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
  journal={Findings of EMNLP 2020},
  year={2020}
}
```
### Contributions

Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
