Datasets:

GEM
/

FairytaleQA

@@ -1,203 +1,205 @@
----
-annotations_creators:
-- expert-generated
-language_creators:
-- found
-languages:
-- en
-licenses:
-- unknown
-multilinguality:
-- monolingual
-pretty_name: FairytaleQA
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- question-generation
-task_ids:
-- abstractive-qg
----
-# Dataset Card for FairytaleQA
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-instances)
-  - [Data Splits](#data-instances)
-- [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)
-## Dataset Description
-- **Homepage:** [Needs More Information]
-- **Repository:** https://github.com/uci-soe/FairytaleQAData
-- **Paper:** https://arxiv.org/abs/2203.13947
-- **Leaderboard:** https://paperswithcode.com/dataset/fairytaleqa
-- **Point of Contact:** [Ying Xu](mailto:ying.xu@uci.edu)
-### 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. This GEM version FairytaleQA Dataset specifically supports the Question Generation task.
-### Supported Tasks and Leaderboards
-- `question-generation`: The dataset can be used to train a model for Question Generation. 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. 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. This task has an active leaderboard which can be found at <https://paperswithcode.com/sota/question-generation-on-fairytaleqa> and ranks models based on [ROUGE](https://huggingface.co/metrics/rouge) score.
-### Languages
-The text in the dataset is in English. The associated BCP-47 code is `en`.
-## Dataset Structure
-### Data Instances
-A typical data point comprises a question, the corresponding story content, and one answer for train split or two answers for valid/test splits. 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.
-An example from the FairytaleQA test split looks as follows:
-```
-{'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 Fields
-- `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.
-### Data Splits
-The data is split into a training, validation, and test split randomly but we control the ratio of QA-pair numbers in train:valid:test splits close to 8:1:1. The final split sizes are as follows:
-|            | Train | Validation | Test  |
-| -----      | ----- | -----      | ----- |
-| # Books    | 232   | 23         | 23    |
-| # QA-Pairs | 8548  | 1025       |1007   |
-## Dataset Creation
-### Curation Rationale
-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.
-<!--We employed education experts to generate and classify 10,580 QA-pairs based on a collection of 278 fairytale stories for young readers. -->
-### Source Data
-#### Initial Data Collection and Normalization
-The narrative texts utilized in the dataset are classic fairytales with clear narrative structures. We gathered the text from the [Project Gutenberg](https://www.gutenberg.org/) website, using “fairytale” as the search term. 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). 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.
-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.
-#### Who are the source language producers?
-The fairytale story texts are from the [Project Gutenberg](https://www.gutenberg.org/) website
-### Annotations
-#### Annotation process
-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.
-#### Who are the annotators?
-Five annotators were involved in the annotation of QA pairs. 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.
-### Personal and Sensitive Information
-[N/A]
-## Considerations for Using the Data
-### Social Impact of Dataset
-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 specific GEM version of our FairytaleQA dataset is for the Question Generation task because this dataset is suitable for developing models to automatically generate questions 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.
-### Discussion of Biases
-This dataset could also be served to identify social stereotypes represented in story narratives. It will be valuable to analyze how social stereotypes are represented in the children’s literature collected in our dataset, thus enabling the development of automatic systems that detect and mitigate social biases. This type of bias analysis has been an underexplored research topic for the ML community, yet it will have profound societal impacts.
-### Other Known Limitations
-[N/A]
-## Additional Information
-### Dataset Curators
-The dataset was created by Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Jia-Jun Li, Nora Bradford, Branda Sun, Tran Bao Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, and Mark Warschauer.
-Schmidt Futures provided funding for the development of the FairytaleQA dataset.
-### Licensing Information
-[N/A]
-### Citation Information
-```
-@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-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: FairytaleQA
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-generation
+task_ids:
+- abstractive-qg
+---
+# Dataset Card for FairytaleQA
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+## Dataset Description
+- **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/uci-soe/FairytaleQAData
+- **Paper:** https://arxiv.org/abs/2203.13947
+- **Leaderboard:** https://paperswithcode.com/dataset/fairytaleqa
+- **Point of Contact:** [Ying Xu](mailto:ying.xu@uci.edu)
+### 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. This GEM version FairytaleQA Dataset specifically supports the Question Generation task.
+### Supported Tasks and Leaderboards
+- `question-generation`: The dataset can be used to train a model for Question Generation. 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. 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. This task has an active leaderboard which can be found at <https://paperswithcode.com/sota/question-generation-on-fairytaleqa> and ranks models based on [ROUGE](https://huggingface.co/metrics/rouge) score.
+### Languages
+The text in the dataset is in English. The associated BCP-47 code is `en`.
+## Dataset Structure
+### Data Instances
+A typical data point comprises a question, the corresponding story content, and one answer for train split or two answers for valid/test splits. 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 established framework.
+For the original question file of each story, human annotator may provide more than one answer. However, to maintain evaluation fairness across all QAs, we only load the first answer here for the QG task.
+An example from the FairytaleQA test split looks as follows:
+```
+{'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 Fields
+- `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.
+### Data Splits
+The data is split into a training, validation, and test split randomly but we control the ratio of QA-pair numbers in train:valid:test splits close to 8:1:1. The final split sizes are as follows:
+|            | Train | Validation | Test  |
+| -----      | ----- | -----      | ----- |
+| # Books    | 232   | 23         | 23    |
+| # QA-Pairs | 8548  | 1025       |1007   |
+## Dataset Creation
+### Curation Rationale
+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.
+<!--We employed education experts to generate and classify 10,580 QA-pairs based on a collection of 278 fairytale stories for young readers. -->
+### Source Data
+#### Initial Data Collection and Normalization
+The narrative texts utilized in the dataset are classic fairytales with clear narrative structures. We gathered the text from the [Project Gutenberg](https://www.gutenberg.org/) website, using “fairytale” as the search term. 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). 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.
+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.
+#### Who are the source language producers?
+The fairytale story texts are from the [Project Gutenberg](https://www.gutenberg.org/) website
+### Annotations
+#### Annotation process
+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.
+#### Who are the annotators?
+Five annotators were involved in the annotation of QA pairs. 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.
+### Personal and Sensitive Information
+[N/A]
+## Considerations for Using the Data
+### Social Impact of Dataset
+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 specific GEM version of our FairytaleQA dataset is for the Question Generation task because this dataset is suitable for developing models to automatically generate questions 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.
+### Discussion of Biases
+This dataset could also be served to identify social stereotypes represented in story narratives. It will be valuable to analyze how social stereotypes are represented in the children’s literature collected in our dataset, thus enabling the development of automatic systems that detect and mitigate social biases. This type of bias analysis has been an underexplored research topic for the ML community, yet it will have profound societal impacts.
+### Other Known Limitations
+[N/A]
+## Additional Information
+### Dataset Curators
+The dataset was created by Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Jia-Jun Li, Nora Bradford, Branda Sun, Tran Bao Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, and Mark Warschauer.
+Schmidt Futures provided funding for the development of the FairytaleQA dataset.
+### Licensing Information
+[N/A]
+### Citation Information
+```
+@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}
+}
 ```