task_categories:other annotations_creators:expert-created language_creators:unknown multilinguality:unknown size_categories:unknown source_datasets:original language:en license:unknown question-generation arxiv:2203.13947 region:us
\
The FairytaleQA dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. This is for the Question Generation Task of FairytaleQA.
\
@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}
}
---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- en
license:
- unknown
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: FairytaleQA
tags:
- question-generation
---

# Dataset Card for GEM/FairytaleQA

## Dataset Description

- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/uci-soe/FairytaleQAData
- **Paper:** https://arxiv.org/abs/2203.13947
- **Leaderboard:** https://paperswithcode.com/sota/question-generation-on-fairytaleqa
- **Point of Contact:** Ying Xu, Dakuo Wang

### Link to Main Data Card

You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/FairytaleQA).

### Dataset Summary 

The FairytaleQA Dataset is an English-language dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. The Dataset was corrected to support both the tasks of Question Generation and Question Answering.

You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/FairytaleQA')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/FairytaleQA).

#### paper
[ArXiv](https://arxiv.org/abs/2203.13947)

#### authors
Ying Xu (University of California Irvine); Dakuo Wang (IBM Research); Mo Yu (IBM Research); Daniel Ritchie (University of California Irvine); Bingsheng Yao (Rensselaer Polytechnic Institute); Tongshuang Wu (University of Washington); Zheng Zhang (University of Notre Dame); Toby Jia-Jun Li (University of Notre Dame); Nora Bradford (University of California Irvine); Branda Sun (University of California Irvine); Tran Bao Hoang (University of California Irvine); Yisi Sang (Syracuse University); Yufang Hou (IBM Research Ireland); Xiaojuan Ma (Hong Kong Univ. of Sci and Tech); Diyi Yang (Georgia Institute of Technology); Nanyun Peng (University of California Los Angeles); Zhou Yu (Columbia University); Mark Warschauer (University of California Irvine)

## Dataset Overview

### Where to find the Data and its Documentation

#### Download

<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/uci-soe/FairytaleQAData)

#### Paper

<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ArXiv](https://arxiv.org/abs/2203.13947)

#### BibTex

<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
@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}
}

#### Contact Name

<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Ying Xu, Dakuo Wang

#### Contact Email

<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
ying.xu@uci.edu, dakuo.wang@ibm.com

#### Has a Leaderboard?

<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes

#### Leaderboard Link

<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
[PapersWithCode](https://paperswithcode.com/sota/question-generation-on-fairytaleqa)

#### Leaderboard Details

<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
The task was to generate questions corresponding to the given answers and the story context. Success on the Question Generation task is typically measured by achieving a high ROUGE-L score to the reference ground-truth question.


### Languages and Intended Use

#### Multilingual?

<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no

#### Covered Dialects

<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
[N/A]

#### Covered Languages

<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`

#### Whose Language?

<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
[N/A]

#### License

<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
unknown: License information unavailable

#### Intended Use

<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The purpose of this dataset is to help develop systems to facilitate assessment and training of narrative comprehension skills for children in education domain. The dataset distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way. 

This dataset is suitable for developing models to automatically generate questions and QA-Pairs that satisfy the need for a continuous supply of new questions, which can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.

#### Primary Task

<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Question Generation

#### Communicative Goal

<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
The task was to generate questions corresponding to the given answers and the story context. Models trained for this task can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.


### Credit

#### Curation Organization Type(s)

<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`

#### Curation Organization(s)

<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
University of California Irvine

#### Dataset Creators

<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Ying Xu (University of California Irvine); Dakuo Wang (IBM Research); Mo Yu (IBM Research); Daniel Ritchie (University of California Irvine); Bingsheng Yao (Rensselaer Polytechnic Institute); Tongshuang Wu (University of Washington); Zheng Zhang (University of Notre Dame); Toby Jia-Jun Li (University of Notre Dame); Nora Bradford (University of California Irvine); Branda Sun (University of California Irvine); Tran Bao Hoang (University of California Irvine); Yisi Sang (Syracuse University); Yufang Hou (IBM Research Ireland); Xiaojuan Ma (Hong Kong Univ. of Sci and Tech); Diyi Yang (Georgia Institute of Technology); Nanyun Peng (University of California Los Angeles); Zhou Yu (Columbia University); Mark Warschauer (University of California Irvine)

#### Funding

<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Schmidt Futures

#### Who added the Dataset to GEM?

<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Dakuo Wang (IBM Research); Bingsheng Yao (Rensselaer Polytechnic Institute); Ying Xu (University of California Irvine)


### Dataset Structure

#### Data Fields

<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `story_name`: a string of the story name to which the story section content belongs. Full story data can be found [here](https://github.com/uci-soe/FairytaleQAData).

- `content`: a string of the story section(s) content related to the experts' labeled QA-pair. Used as the input for both Question Generation and Question Answering tasks.  

- `question`: a string of the question content. Used as the input for Question Answering task and as the output for Question Generation task.  

- `answer`: a string of the answer content for all splits. Used as the input for Question Generation task and as the output for Question Answering task.

- `gem_id`: a string of id follows GEM naming convention ```GEM-${DATASET_NAME}-${SPLIT-NAME}-${id}``` where id is an incrementing number starting at 1

- `target`: a string of the question content being used for training

- `references`: a list of string containing the question content being used for automatic eval

- `local_or_sum`: a string of either local or summary, indicating whether the QA is related to one story section or multiple sections  

- `attribute`: a string of one of character, causal relationship, action, setting, feeling, prediction, or outcome resolution. Classification of the QA by education experts annotators via 7 narrative elements on an established framework
 
- `ex_or_im`: a string of either explicit or implicit, indicating whether the answers can be directly found in the story content or cannot be directly from the story content.


#### Reason for Structure

<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
[N/A]

#### How were labels chosen?

<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
A typical data point comprises a question, the corresponding story content, and one answer. Education expert annotators labeled whether the answer is locally relevant to one story section or requires summarization capabilities from multiple story sections, and whether the answers are explicit (can be directly found in the stories) or implicit (cannot be directly found in the story text). Additionally, education expert annotators categorize the QA-pairs via 7 narrative elements from an establish framework.

#### Example Instance

<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
{'story_name': 'self-did-it', 
'content': '" what is your name ? " asked the girl from underground . " self is my name , " said the woman . that seemed a curious name to the girl , and she once more began to pull the fire apart . then the woman grew angry and began to scold , and built it all up again . thus they went on for a good while ; but at last , while they were in the midst of their pulling apart and building up of the fire , the woman upset the tar - barrel on the girl from underground . then the latter screamed and ran away , crying : " father , father ! self burned me ! " " nonsense , if self did it , then self must suffer for it ! " came the answer from below the hill .', 
'answer': 'the woman told the girl her name was self .', 
'question': "why did the girl's father think the girl burned herself ?", 
'gem_id': 'GEM-FairytaleQA-test-1006', 
'target': "why did the girl's father think the girl burned herself ?", 
'references': ["why did the girl's father think the girl burned herself ?"], 
'local_or_sum': 'local', 
'attribute': 'causal relationship', 
'ex_or_im': 'implicit'}

#### Data Splits

<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The data is split into a train, validation, and test split randomly. The final split sizes are as follows:

|            | Train | Validation | Test  |
| -----      | ----- | -----      | ----- |
| # Books    | 232   | 23         | 23    |
| # QA-Pairs | 8548  | 1025       |1007   |

#### Splitting Criteria

<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The books are randomly split into train/validation/test splits. We control the ratio of QA-pair numbers in train:validation:test splits close to 8:1:1

#### 

<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
[N/A]



## Dataset in GEM

### Rationale for Inclusion in GEM

#### Why is the Dataset in GEM?

<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The dataset distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way.



#### Similar Datasets

<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no

#### Ability that the Dataset measures

<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
This dataset is suitable for developing models to automatically generate questions or QA-pairs that satisfy the need for a continuous supply of new questions, which can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.


### GEM-Specific Curation

#### Modificatied for GEM?

<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes

#### GEM Modifications

<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points removed`

#### Modification Details

<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
The original data contains two answers by different annotators in validation/test splits, we removed the 2nd answer for GEM version because it is not being used for the Question Generation task.  

#### Additional Splits?

<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no


### Getting Started with the Task

#### Pointers to Resources

<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
[N/A]



## Previous Results

### Previous Results

#### Measured Model Abilities

<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
We are able to measure model's capabilities of generating various types of questions that corresponds to different narrative elements with the FairytaleQA dataset on the Question Generation Task

#### Metrics

<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`

#### Proposed Evaluation

<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
The task was to generate questions corresponding to the given answers and the story context. Success on this task is typically measured by achieving a high [ROUGE](https://huggingface.co/metrics/rouge) score to the reference ground-truth questions. 

#### Previous results available?

<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes

#### Relevant Previous Results

<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
A [BART-based model](https://huggingface.co/facebook/bart-large) currently achieves a [ROUGE-L of 0.527/0.527](https://github.com/uci-soe/FairytaleQAData) on valid/test splits, which is reported as the baseline experiment for the dataset [paper](https://arxiv.org/pdf/2203.13947.pdf).



## Dataset Curation

### Original Curation

#### Original Curation Rationale

<!-- info: Original curation rationale -->
<!-- scope: telescope -->
FairytaleQA was built to focus on comprehension of narratives in the education domain, targeting students from kindergarten to eighth grade. We focus on narrative comprehension for 1. it is a high-level comprehension skill strongly predictive of reading achievement and plays a central role in daily life as people frequently encounter narratives in different forms, 2. narrative stories have a clear structure of specific elements and relations among these elements, and there are existing validated narrative comprehension frameworks around this structure, which provides a basis for developing the annotation schema for our dataset.

#### Communicative Goal

<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The purpose of this dataset is to help develop systems to facilitate assessment and training of narrative comprehension skills for children in education domain.

#### Sourced from Different Sources

<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no


### Language Data

#### How was Language Data Obtained?

<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`

#### Where was it found?

<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`

#### Language Producers

<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The fairytale story texts are from the [Project Gutenberg](https://www.gutenberg.org/) website

#### Topics Covered

<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
We gathered the text from the Project Gutenberg website, using “fairytale” as the search term. 

#### Data Validation

<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator

#### Data Preprocessing

<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Due to a large number of fairytales found, we used the most popular stories based on the number of downloads since these stories are presumably of higher quality. To ensure the readability of the text, we made a small number of minor revisions to some obviously outdated vocabulary (e.g., changing “ere” to “before”) and the unconventional use of punctuation (e.g., changing consecutive semi-colons to periods). 

These texts were broken down into small sections based on their semantic content by our annotators. The annotators were instructed to split the story into sections of 100-300 words that also contain meaningful content and are separated at natural story breaks. An initial annotator would split the story, and this would be reviewed by a cross-checking annotator. Most of the resulting sections were one natural paragraph of the original text.

#### Was Data Filtered?

<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
manually

#### Filter Criteria

<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
For each story, we evaluated the reading difficulty level using the [textstat](https://pypi.org/project/textstat/) Python package, primarily based on sentence length, word length, and commonness of words. We excluded stories that are at 10th grade level or above.


### Structured Annotations

#### Additional Annotations?

<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
expert created

#### Number of Raters

<!-- info: What is the number of raters -->
<!-- scope: telescope -->
2<n<10

#### Rater Qualifications

<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
All of these annotators have a B.A. degree in education, psychology, or cognitive science and have substantial experience in teaching and reading assessment. These annotators were supervised by three experts in literacy education.

#### Raters per Training Example

<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
2

#### Raters per Test Example

<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
3

#### Annotation Service?

<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no

#### Annotation Values

<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
The dataset annotation distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way.

#### Any Quality Control?

<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by data curators

#### Quality Control Details

<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
The annotators were instructed to imagine that they were creating questions to test elementary or middle school students in the process of reading a complete story. We required the annotators to generate only natural, open-ended questions, avoiding “yes-” or “no-” questions. We also instructed them to provide a diverse set of questions about 7 different narrative elements, and with both implicit and explicit questions. 

We asked the annotators to also generate answers for each of their questions. We asked them to provide the shortest possible answers but did not restrict them to complete sentences or short phrases. We also asked the annotators to label which section(s) the question and answer was from.

All annotators received a two-week training in which each of them was familiarized with the coding template and conducted practice coding on the same five stories. The practice QA pairs were then reviewed by the other annotators and the three experts, and discrepancies among annotators were discussed. During the annotation process, the team met once every week to review and discuss each member’s work. All QA pairs were cross-checked by two annotators, and 10% of the QA pairs were additionally checked by the expert supervisor.

For the 46 stories used as the evaluation set, we annotate a second reference answer by asking an annotator to independently read the story and answer the questions generated by others.


### Consent

#### Any Consent Policy?

<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes

#### Consent Policy Details

<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
During the annotation process, the team met once every week to review and discuss each member’s work. All QA pairs were cross-checked by two annotators, and 10% of the QA pairs were additionally checked by the expert supervisor.

#### Other Consented Downstream Use

<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
<!-- scope: microscope -->
Aside from Question Generation task, the data creators and curators used this data for Question Answering, and QA-Pair Generation tasks, and to identify social stereotypes represented in story narratives.


### Private Identifying Information (PII)

#### Contains PII?

<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII

#### Justification for no PII

<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The story content is from publically available knowledge website and the annotated QA-pairs are about general knowledge to the story content without references to the author or to any persons


### Maintenance

#### Any Maintenance Plan?

<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
yes

#### Maintenance Plan Details

<!-- info: Describe the original dataset's maintenance plan. -->
<!-- scope: microscope -->
We plan to host various splits for the FairytaleQA dataset to better serve various types of research interests. We have the original data for 2 different split approaches including train/validation/test splits and split by fairytale origins. We are also plan to host the dataset on multiple platforms for various tasks. 

#### Maintainer Contact Information

<!-- info: Provide contact information of a person responsible for the dataset maintenance -->
<!-- scope: periscope -->
Daniel Ritchie

#### Any Contestation Mechanism?

<!-- info: Does the maintenance plan include a contestation mechanism allowing individuals to request removal fo content? -->
<!-- scope: periscope -->
no mechanism



## Broader Social Context

### Previous Work on the Social Impact of the Dataset

#### Usage of Models based on the Data

<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
yes - models trained on this dataset

#### Social Impact Observations

<!-- info: Did any of these previous uses result in observations about the social impact of the systems? In particular, has there been work outlining the risks and limitations of the system? Provide links and descriptions here. -->
<!-- scope: microscope -->
[N/A]

#### Changes as Consequence of Social Impact

<!-- info: Have any changes been made to the dataset as a result of these observations? -->
<!-- scope: periscope -->
[N/A]


### Impact on Under-Served Communities

#### Addresses needs of underserved Communities?

<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes

#### Details on how Dataset Addresses the Needs

<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
From the educational perspective, given that reading comprehension is a multicomponent skill, it is ideal for comprehension questions to be able to identify students’ performance in specific sub-skills, thus allowing teachers to provide tailored guidance. 


### Discussion of Biases

#### Any Documented Social Biases?

<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure

#### Are the Language Producers Representative of the Language?

<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
[N/A]



## Considerations for Using the Data

### PII Risks and Liability

#### Potential PII Risk

<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
[N/A]


### Licenses

#### Copyright Restrictions on the Dataset

<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`research use only`

#### Copyright Restrictions on the Language Data

<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`


### Known Technical Limitations

#### Technical Limitations

<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
We noticed that human results are obtained via cross-estimation between the two annotated answers, thus are underestimated. One possibility for future work is to conduct a large-scale human annotation to collect more answers per question and then leverage the massively annotated answers to better establish a human performance evaluation.

#### Unsuited Applications

<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
The QA-pairs annotated by education experts are targeting the audience of children from kindergarten to eighth grade, so the difficulty of QA-pairs are not suitable to compare with other existing dataset that are sourced from knowledge graphs or knowledge bases like Wikipedia.

#### Discouraged Use Cases

<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
[N/A]


