
# Dataset Card for QA2D

## 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:** https://worksheets.codalab.org/worksheets/0xd4ebc52cebb84130a07cbfe81597aaf0/
- **Repository:** https://github.com/kelvinguu/qanli
- **Paper:** https://arxiv.org/abs/1809.02922
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.

This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.

### Supported Tasks and Leaderboards

[Needs More Information]

### Languages

en

## Dataset Structure

### Data Instances

See below.

### Data Fields

- `dataset`: lowercased name of dataset (movieqa, newsqa, qamr, race, squad)
- `example_uid`: unique id of example within dataset (there are examples with the same uids from different datasets, so the combination of dataset + example_uid should be used for unique indexing)
- `question`: tokenized (space-separated) question from the source QA dataset
- `answer`: tokenized (space-separated) answer span from the source QA dataset
- `turker_answer`: tokenized (space-separated) answer sentence collected from MTurk
- `rule-based`: tokenized (space-separated) answer sentence, generated by the rule-based model

### Data Splits
| Dataset Split | Number of Instances in Split |
| ------------- |----------------------------- |
| Train         | 60,710                       |
| Dev           | 10,344                       |

## Dataset Creation

### Curation Rationale

This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

[Needs More Information]

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

[Needs More Information]

### Citation Information

@article{DBLP:journals/corr/abs-1809-02922,
  author    = {Dorottya Demszky and
               Kelvin Guu and
               Percy Liang},
  title     = {Transforming Question Answering Datasets Into Natural Language Inference
               Datasets},
  journal   = {CoRR},
  volume    = {abs/1809.02922},
  year      = {2018},
  url       = {http://arxiv.org/abs/1809.02922},
  eprinttype = {arXiv},
  eprint    = {1809.02922},
  timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1809-02922.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
task_categories:text2text-generation task_ids:text-simplification annotations_creators:machine-generated annotations_creators:crowdsourced annotations_creators:found language_creators:machine-generated language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original source_datasets:extended|squad source_datasets:extended|race source_datasets:extended|newsqa source_datasets:extended|qamr source_datasets:extended|movieQA license:mit arxiv:1809.02922 region:us
{"annotations_creators": ["machine-generated", "crowdsourced", "found"], "language_creators": ["machine-generated", "crowdsourced"], "language": [], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original", "extended|squad", "extended|race", "extended|newsqa", "extended|qamr", "extended|movieQA"], "task_categories": ["text2text-generation"], "task_ids": ["text-simplification"], "pretty_name": "QA2D"}
TAGS
#task_categories-text2text-generation #task_ids-text-simplification #annotations_creators-machine-generated #annotations_creators-crowdsourced #annotations_creators-found #language_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #source_datasets-extended|squad #source_datasets-extended|race #source_datasets-extended|newsqa #source_datasets-extended|qamr #source_datasets-extended|movieQA #license-mit #arxiv-1809.02922 #region-us 

Dataset Card for QA2D
=====================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ 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
	+ Citation Information


Dataset Description
-------------------


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


### Dataset Summary


Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.


This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.


### Supported Tasks and Leaderboards


### Languages


en


Dataset Structure
-----------------


### Data Instances


See below.


### Data Fields


* 'dataset': lowercased name of dataset (movieqa, newsqa, qamr, race, squad)
* 'example\_uid': unique id of example within dataset (there are examples with the same uids from different datasets, so the combination of dataset + example\_uid should be used for unique indexing)
* 'question': tokenized (space-separated) question from the source QA dataset
* 'answer': tokenized (space-separated) answer span from the source QA dataset
* 'turker\_answer': tokenized (space-separated) answer sentence collected from MTurk
* 'rule-based': tokenized (space-separated) answer sentence, generated by the rule-based model


### Data Splits



Dataset Creation
----------------


### Curation Rationale


This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.


### 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


@article{DBLP:journals/corr/abs-1809-02922,
author = {Dorottya Demszky and
Kelvin Guu and
Percy Liang},
title = {Transforming Question Answering Datasets Into Natural Language Inference
Datasets},
journal = {CoRR},
volume = {abs/1809.02922},
year = {2018},
url = {URL
eprinttype = {arXiv},
eprint = {1809.02922},
timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
biburl = {URL
bibsource = {dblp computer science bibliography, URL}
}



### Dataset Summary


Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.


This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets. ### Supported Tasks and Leaderboards ### Languages


en


Dataset Structure
----------------- ### Data Instances


See below. ### Data Fields


* 'dataset': lowercased name of dataset (movieqa, newsqa, qamr, race, squad)
* 'example\_uid': unique id of example within dataset (there are examples with the same uids from different datasets, so the combination of dataset + example\_uid should be used for unique indexing)
* 'question': tokenized (space-separated) question from the source QA dataset
* 'answer': tokenized (space-separated) answer span from the source QA dataset
* 'turker\_answer': tokenized (space-separated) answer sentence collected from MTurk
* 'rule-based': tokenized (space-separated) answer sentence, generated by the rule-based model ### Data Splits



Dataset Creation
---------------- ### Curation Rationale


This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets. ### 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


@article{DBLP:journals/corr/abs-1809-02922,
author = {Dorottya Demszky and
Kelvin Guu and
Percy Liang},
title = {Transforming Question Answering Datasets Into Natural Language Inference
Datasets},
journal = {CoRR},
volume = {abs/1809.02922},
year = {2018},
url = {URL
eprinttype = {arXiv},
eprint = {1809.02922},
timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
biburl = {URL
bibsource = {dblp computer science bibliography, URL}
}
TAGS
#task_categories-text2text-generation #task_ids-text-simplification #annotations_creators-machine-generated #annotations_creators-crowdsourced #annotations_creators-found #language_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #source_datasets-extended|squad #source_datasets-extended|race #source_datasets-extended|newsqa #source_datasets-extended|qamr #source_datasets-extended|movieQA #license-mit #arxiv-1809.02922 #region-us 
 ### Dataset Summary


Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.


This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets. ### Supported Tasks and Leaderboards ### Languages


en


Dataset Structure
----------------- ### Data Instances


See below. ### Data Fields


* 'dataset': lowercased name of dataset (movieqa, newsqa, qamr, race, squad)
* 'example\_uid': unique id of example within dataset (there are examples with the same uids from different datasets, so the combination of dataset + example\_uid should be used for unique indexing)
* 'question': tokenized (space-separated) question from the source QA dataset
* 'answer': tokenized (space-separated) answer span from the source QA dataset
* 'turker\_answer': tokenized (space-separated) answer sentence collected from MTurk
* 'rule-based': tokenized (space-separated) answer sentence, generated by the rule-based model ### Data Splits



Dataset Creation
---------------- ### Curation Rationale


This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets. ### 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


@article{DBLP:journals/corr/abs-1809-02922,
author = {Dorottya Demszky and
Kelvin Guu and
Percy Liang},
title = {Transforming Question Answering Datasets Into Natural Language Inference
Datasets},
journal = {CoRR},
volume = {abs/1809.02922},
year = {2018},
url = {URL
eprinttype = {arXiv},
eprint = {1809.02922},
timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
biburl = {URL
bibsource = {dblp computer science bibliography, URL}
}
passage: TAGS
#task_categories-text2text-generation #task_ids-text-simplification #annotations_creators-machine-generated #annotations_creators-crowdsourced #annotations_creators-found #language_creators-machine-generated #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #source_datasets-extended|squad #source_datasets-extended|race #source_datasets-extended|newsqa #source_datasets-extended|qamr #source_datasets-extended|movieQA #license-mit #arxiv-1809.02922 #region-us 
### Dataset Summary


Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.


This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.### Supported Tasks and Leaderboards### Languages


en


Dataset Structure
-----------------### Data Instances


See below.