
# Dataset Card for FairytaleQA

## Dataset Description

- **Homepage:** 
- **Repository:**
 https://github.com/uci-soe/FairytaleQAData
 https://github.com/WorkInTheDark/FairytaleQA_Dataset
- **Paper:**
 https://aclanthology.org/2022.acl-long.34/
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This is the repository for the FairytaleQA dataset, an open-source dataset focusing on comprehension of narratives, targeting students from kindergarten to eighth grade. The FairytaleQA dataset is annotated by education experts based on an evidence-based theoretical framework. It consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

### Supported Tasks and Leaderboards

Question-Answering, Question-Generation, Question-Answer Pair Generation

### Languages

English

## Dataset Structure


### Data Instances

An example of "train" looks as follows:

```

{
  'story_name': 'three-dogs',
  'story_section': 'once upon a time there was a king who went forth into the world and 
                      ... ...
                    guards to watch over the little princess so that she would not get out under the open sky .',
  'question': 'why was there great rejoicing in the city and throughout the country ?',
  'answer1': 'the people wished their king all that was good .',
  'answer2': '',
  'local-or-sum': 'local',
  'attribute': 'causal relationship',
  'ex-or-im': 'explicit',
  'ex-or-im2': '',
}

```

### Data Fields

 - **'story_name'**: story name
 - **'story_section'**: story section related to the QA-pair
 - **'question'**: the question content
 - **'answer1'**: the 1st answer  (available in all splits)
 - **'answer2'**: the 2nd answer by another annotator (only available in test / val splits)
 - **'local-or-sum'**: 'local' denotes the question is related to only one story section, while 'summary' denotes the question is related to multiple story sections
 - **'attribute'**: categorized by education experts into seven narrative elements: character / setting / action / feeling / causal relationship / outcome resolution, detailed definition is described in the paper
 - **'ex-or-im'**: 'explicit' denotes the answer can be found in the story content, while 'implicit' denotes the answer require high-level summarization
 - **'ex-or-im2'**: similar to 'ex-or-im', but annotated by another annotator (only available in storys in test / val splits)



### Data Splits

- train split: 232 books with 8548 QA-pairs
- val split: 23 books with 1025 QA-pairs
- test split: 23 books with 1007 QA-pairs

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### 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

[More Information Needed]

### Citation Information

Our Dataset Paper is accepted to ACL 2022, you may cite:
```
@inproceedings{xu2022fairytaleqa,
    author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and Yu, Zhou and Warschauer, Mark},
    title = {Fantastic Questions and Where to Find Them: Fairytale{QA} -- An Authentic Dataset for Narrative Comprehension},
    publisher = {Association for Computational Linguistics},
    year = {2022}
}
```

### Contributions

[More Information Needed]
task_categories:question-answering task_categories:text-generation language:en license:apache-2.0 education children education region:us
{"language": ["en"], "license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "tags": ["education", "children education"]}
TAGS
#task_categories-question-answering #task_categories-text-generation #language-English #license-apache-2.0 #education #children education #region-us 


# Dataset Card for FairytaleQA

## Dataset Description

- Homepage: 
- Repository:
 URL
 URL
- Paper:
 URL
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

This is the repository for the FairytaleQA dataset, an open-source dataset focusing on comprehension of narratives, targeting students from kindergarten to eighth grade. The FairytaleQA dataset is annotated by education experts based on an evidence-based theoretical framework. It consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

### Supported Tasks and Leaderboards

Question-Answering, Question-Generation, Question-Answer Pair Generation

### Languages

English

## Dataset Structure


### Data Instances

An example of "train" looks as follows:



### Data Fields

 - 'story_name': story name
 - 'story_section': story section related to the QA-pair
 - 'question': the question content
 - 'answer1': the 1st answer  (available in all splits)
 - 'answer2': the 2nd answer by another annotator (only available in test / val splits)
 - 'local-or-sum': 'local' denotes the question is related to only one story section, while 'summary' denotes the question is related to multiple story sections
 - 'attribute': categorized by education experts into seven narrative elements: character / setting / action / feeling / causal relationship / outcome resolution, detailed definition is described in the paper
 - 'ex-or-im': 'explicit' denotes the answer can be found in the story content, while 'implicit' denotes the answer require high-level summarization
 - 'ex-or-im2': similar to 'ex-or-im', but annotated by another annotator (only available in storys in test / val splits)



### Data Splits

- train split: 232 books with 8548 QA-pairs
- val split: 23 books with 1025 QA-pairs
- test split: 23 books with 1007 QA-pairs

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information





Our Dataset Paper is accepted to ACL 2022, you may cite:


### Contributions


# Dataset Card for FairytaleQA ## Dataset Description

- Homepage: 
- Repository:
 URL
 URL
- Paper:
 URL
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This is the repository for the FairytaleQA dataset, an open-source dataset focusing on comprehension of narratives, targeting students from kindergarten to eighth grade. The FairytaleQA dataset is annotated by education experts based on an evidence-based theoretical framework. It consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. ### Supported Tasks and Leaderboards

Question-Answering, Question-Generation, Question-Answer Pair Generation ### Languages

English ## Dataset Structure ### Data Instances

An example of "train" looks as follows: ### Data Fields

 - 'story_name': story name
 - 'story_section': story section related to the QA-pair
 - 'question': the question content
 - 'answer1': the 1st answer  (available in all splits)
 - 'answer2': the 2nd answer by another annotator (only available in test / val splits)
 - 'local-or-sum': 'local' denotes the question is related to only one story section, while 'summary' denotes the question is related to multiple story sections
 - 'attribute': categorized by education experts into seven narrative elements: character / setting / action / feeling / causal relationship / outcome resolution, detailed definition is described in the paper
 - 'ex-or-im': 'explicit' denotes the answer can be found in the story content, while 'implicit' denotes the answer require high-level summarization
 - 'ex-or-im2': similar to 'ex-or-im', but annotated by another annotator (only available in storys in test / val splits) ### Data Splits

- train split: 232 books with 8548 QA-pairs
- val split: 23 books with 1025 QA-pairs
- test split: 23 books with 1007 QA-pairs ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information





Our Dataset Paper is accepted to ACL 2022, you may cite: ### Contributions
TAGS
#task_categories-question-answering #task_categories-text-generation #language-English #license-apache-2.0 #education #children education #region-us 
 # Dataset Card for FairytaleQA ## Dataset Description

- Homepage: 
- Repository:
 URL
 URL
- Paper:
 URL
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This is the repository for the FairytaleQA dataset, an open-source dataset focusing on comprehension of narratives, targeting students from kindergarten to eighth grade. The FairytaleQA dataset is annotated by education experts based on an evidence-based theoretical framework. It consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. ### Supported Tasks and Leaderboards

Question-Answering, Question-Generation, Question-Answer Pair Generation ### Languages

English ## Dataset Structure ### Data Instances

An example of "train" looks as follows: ### Data Fields

 - 'story_name': story name
 - 'story_section': story section related to the QA-pair
 - 'question': the question content
 - 'answer1': the 1st answer  (available in all splits)
 - 'answer2': the 2nd answer by another annotator (only available in test / val splits)
 - 'local-or-sum': 'local' denotes the question is related to only one story section, while 'summary' denotes the question is related to multiple story sections
 - 'attribute': categorized by education experts into seven narrative elements: character / setting / action / feeling / causal relationship / outcome resolution, detailed definition is described in the paper
 - 'ex-or-im': 'explicit' denotes the answer can be found in the story content, while 'implicit' denotes the answer require high-level summarization
 - 'ex-or-im2': similar to 'ex-or-im', but annotated by another annotator (only available in storys in test / val splits) ### Data Splits

- train split: 232 books with 8548 QA-pairs
- val split: 23 books with 1025 QA-pairs
- test split: 23 books with 1007 QA-pairs ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information





Our Dataset Paper is accepted to ACL 2022, you may cite: ### Contributions