
# `deepa2` Datasets Collection 

## Table of Contents
- [`deepa2` Datasets Collection](#deepa2-datasets-collection)
  - [Table of Contents](#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)
    - [Sub-Datasets](#sub-datasets)
    - [Data Instances](#data-instances)
    - [Data Fields](#data-fields)
    - [Data Splits](#data-splits)
  - [Dataset Creation](#dataset-creation)
    - [Curation Rationale](#curation-rationale)
    - [Source Data](#source-data)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
      - [Who are the annotators?](#who-are-the-annotators)
    - [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:** [blog post](https://debatelab.github.io/journal/deepa2.html)
- **Repository:** [github](https://github.com/debatelab/deepa2)
- **Paper:** [arxiv](https://arxiv.org/abs/2110.01509)
- **Point of Contact:** [Gregor Betz](gregor.betz@kit.edu)

### Dataset Summary

This is a growing, curated collection of `deepa2` datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises: 

* datasets that are built from existing NLP datasets by means of the [`deepa2 bake`](https://github.com/debatelab/deepa2) tool.
* original `deepa2` datasets specifically created for this collection.

The tool [`deepa2 serve`](https://github.com/debatelab/deepa2#integrating-deepa2-into-your-training-pipeline) may be used to render the data in this collection as text2text examples.  

### Supported Tasks and Leaderboards

For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).

- `conditional-text-generation`: The dataset can be used to train models to generate a fully reconstruction of an argument from a source text, making, e.g., its implicit assumptions explicit.
- `structure-prediction`: The dataset can be used to train models to formalize sentences.
- `text-retrieval`:  The dataset can be used to train models to extract reason statements and conjectures from a given source text.

### Languages

English. Will be extended to cover other languages in the futures.

## Dataset Structure

### Sub-Datasets

This collection contains the following `deepa2` datasets:

* `esnli`: created from e-SNLI with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/esnli.md).
* `enbank` (`task_1`, `task_2`): created from Entailment Bank with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/enbank.md).
* `argq`: created from IBM-ArgQ with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/argq.md).
* `argkp`: created from IBM-KPA with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/argkp.md).
* `aifdb` (`moral-maze`, `us2016`, `vacc-itc`): created from AIFdb with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/aifdb.md).
* `aaac` (`aaac01` and `aaac02`): original, machine-generated contribution; based on an an improved and extended algorithm that backs https://huggingface.co/datasets/debatelab/aaac.

### Data Instances

see: https://github.com/debatelab/deepa2/tree/main/docs

### Data Fields

see: https://github.com/debatelab/deepa2/tree/main/docs

|feature|esnli|enbank|aifdb|aaac|argq|argkp|
|--|--|--|--|--|--|--|
| `source_text` | x | x | x | x | x | x |  
| `title` |   | x |   | x |   |   |
| `gist` | x | x |   | x |   | x |
| `source_paraphrase` | x | x | x | x |   |   |
| `context` |   | x |   | x |   | x |
| `reasons` | x | x | x | x | x |   |
| `conjectures` | x | x | x | x | x |   |
| `argdown_reconstruction` | x | x |   | x |   | x |
| `erroneous_argdown` | x |   |   | x |   |   |
| `premises` | x | x |   | x |   | x |
| `intermediary_conclusion` |   |   |   | x |   |   |
| `conclusion` | x | x |   | x |   | x |
| `premises_formalized` | x |   |   | x |   | x |
| `intermediary_conclusion_formalized` |   |   |   | x |   |   |
| `conclusion_formalized` | x |   |   | x |   | x |
| `predicate_placeholders` |   |   |   | x |   |   |
| `entity_placeholders` |   |   |   | x |   |   |
| `misc_placeholders` | x |   |   | x |   | x |
| `plchd_substitutions` | x |   |   | x |   | x |

### Data Splits

Each sub-dataset contains three splits: `train`, `validation`, and `test`.

## Dataset Creation

### Curation Rationale

Many NLP datasets focus on tasks that are relevant for logical analysis and argument reconstruction. This collection is the attempt to unify these resources in a common framework.

### Source Data

See: [Sub-Datasets](#sub-datasets)

## Additional Information

### Dataset Curators

Gregor Betz, KIT; Kyle Richardson, Allen AI

### Licensing Information

We re-distribute the the imported sub-datasets under their original license:

|Sub-dataset|License|
|--|--|
|esnli|MIT|
|aifdb|free for academic use ([TOU](https://arg-tech.org/index.php/research/argument-corpora/))|
|enbank|CC BY 4.0|
|aaac|CC BY 4.0|
|argq|CC BY SA 4.0|
|argkp|Apache|

### Citation Information

```
@article{betz2021deepa2,
      title={DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models}, 
      author={Gregor Betz and Kyle Richardson},
      year={2021},
      eprint={2110.01509},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

<!--If the dataset has a [DOI](https://www.doi.org/), please provide it here.-->


task_categories:text-retrieval task_categories:text-generation task_ids:text-simplification task_ids:parsing language_creators:other multilinguality:monolingual size_categories:unknown language:en license:other argument-mining summarization conditional-text-generation structure-prediction arxiv:2110.01509 region:us
{"annotations_creators": [], "language_creators": ["other"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-retrieval", "text-generation"], "task_ids": ["text-simplification", "parsing"], "pretty_name": "deepa2", "tags": ["argument-mining", "summarization", "conditional-text-generation", "structure-prediction"]}
TAGS
#task_categories-text-retrieval #task_categories-text-generation #task_ids-text-simplification #task_ids-parsing #language_creators-other #multilinguality-monolingual #size_categories-unknown #language-English #license-other #argument-mining #summarization #conditional-text-generation #structure-prediction #arxiv-2110.01509 #region-us 

'deepa2' Datasets Collection
============================


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


* 'deepa2' Datasets Collection
	+ Table of Contents
	+ Dataset Description
		- Dataset Summary
		- Supported Tasks and Leaderboards
		- Languages
	+ Dataset Structure
		- Sub-Datasets
		- Data Instances
		- Data Fields
		- Data Splits
	+ 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
		- Citation Information
		- Contributions


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


* Homepage: blog post
* Repository: github
* Paper: arxiv
* Point of Contact: Gregor Betz


### Dataset Summary


This is a growing, curated collection of 'deepa2' datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises:


* datasets that are built from existing NLP datasets by means of the 'deepa2 bake' tool.
* original 'deepa2' datasets specifically created for this collection.


The tool 'deepa2 serve' may be used to render the data in this collection as text2text examples.


### Supported Tasks and Leaderboards


For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').


* 'conditional-text-generation': The dataset can be used to train models to generate a fully reconstruction of an argument from a source text, making, e.g., its implicit assumptions explicit.
* 'structure-prediction': The dataset can be used to train models to formalize sentences.
* 'text-retrieval': The dataset can be used to train models to extract reason statements and conjectures from a given source text.


### Languages


English. Will be extended to cover other languages in the futures.


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


### Sub-Datasets


This collection contains the following 'deepa2' datasets:


* 'esnli': created from e-SNLI with 'deepa2 bake' as described here.
* 'enbank' ('task\_1', 'task\_2'): created from Entailment Bank with 'deepa2 bake' as described here.
* 'argq': created from IBM-ArgQ with 'deepa2 bake' as described here.
* 'argkp': created from IBM-KPA with 'deepa2 bake' as described here.
* 'aifdb' ('moral-maze', 'us2016', 'vacc-itc'): created from AIFdb with 'deepa2 bake' as described here.
* 'aaac' ('aaac01' and 'aaac02'): original, machine-generated contribution; based on an an improved and extended algorithm that backs URL


### Data Instances


see: URL


### Data Fields


see: URL



### Data Splits


Each sub-dataset contains three splits: 'train', 'validation', and 'test'.


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


### Curation Rationale


Many NLP datasets focus on tasks that are relevant for logical analysis and argument reconstruction. This collection is the attempt to unify these resources in a common framework.


### Source Data


See: Sub-Datasets


Additional Information
----------------------


### Dataset Curators


Gregor Betz, KIT; Kyle Richardson, Allen AI


### Licensing Information


We re-distribute the the imported sub-datasets under their original license:




### Dataset Summary


This is a growing, curated collection of 'deepa2' datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises:


* datasets that are built from existing NLP datasets by means of the 'deepa2 bake' tool.
* original 'deepa2' datasets specifically created for this collection.


The tool 'deepa2 serve' may be used to render the data in this collection as text2text examples. ### Supported Tasks and Leaderboards


For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').


* 'conditional-text-generation': The dataset can be used to train models to generate a fully reconstruction of an argument from a source text, making, e.g., its implicit assumptions explicit.
* 'structure-prediction': The dataset can be used to train models to formalize sentences.
* 'text-retrieval': The dataset can be used to train models to extract reason statements and conjectures from a given source text. ### Languages


English. Will be extended to cover other languages in the futures.


Dataset Structure
----------------- ### Sub-Datasets


This collection contains the following 'deepa2' datasets:


* 'esnli': created from e-SNLI with 'deepa2 bake' as described here.
* 'enbank' ('task\_1', 'task\_2'): created from Entailment Bank with 'deepa2 bake' as described here.
* 'argq': created from IBM-ArgQ with 'deepa2 bake' as described here.
* 'argkp': created from IBM-KPA with 'deepa2 bake' as described here.
* 'aifdb' ('moral-maze', 'us2016', 'vacc-itc'): created from AIFdb with 'deepa2 bake' as described here.
* 'aaac' ('aaac01' and 'aaac02'): original, machine-generated contribution; based on an an improved and extended algorithm that backs URL ### Data Instances


see: URL ### Data Fields


see: URL ### Data Splits


Each sub-dataset contains three splits: 'train', 'validation', and 'test'.


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


Many NLP datasets focus on tasks that are relevant for logical analysis and argument reconstruction. This collection is the attempt to unify these resources in a common framework. ### Source Data


See: Sub-Datasets


Additional Information
---------------------- ### Dataset Curators


Gregor Betz, KIT; Kyle Richardson, Allen AI ### Licensing Information


We re-distribute the the imported sub-datasets under their original license:
TAGS
#task_categories-text-retrieval #task_categories-text-generation #task_ids-text-simplification #task_ids-parsing #language_creators-other #multilinguality-monolingual #size_categories-unknown #language-English #license-other #argument-mining #summarization #conditional-text-generation #structure-prediction #arxiv-2110.01509 #region-us 
 ### Dataset Summary


This is a growing, curated collection of 'deepa2' datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises:


* datasets that are built from existing NLP datasets by means of the 'deepa2 bake' tool.
* original 'deepa2' datasets specifically created for this collection.


The tool 'deepa2 serve' may be used to render the data in this collection as text2text examples. ### Supported Tasks and Leaderboards


For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').


* 'conditional-text-generation': The dataset can be used to train models to generate a fully reconstruction of an argument from a source text, making, e.g., its implicit assumptions explicit.
* 'structure-prediction': The dataset can be used to train models to formalize sentences.
* 'text-retrieval': The dataset can be used to train models to extract reason statements and conjectures from a given source text. ### Languages


English. Will be extended to cover other languages in the futures.


Dataset Structure
----------------- ### Sub-Datasets


This collection contains the following 'deepa2' datasets:


* 'esnli': created from e-SNLI with 'deepa2 bake' as described here.
* 'enbank' ('task\_1', 'task\_2'): created from Entailment Bank with 'deepa2 bake' as described here.
* 'argq': created from IBM-ArgQ with 'deepa2 bake' as described here.
* 'argkp': created from IBM-KPA with 'deepa2 bake' as described here.
* 'aifdb' ('moral-maze', 'us2016', 'vacc-itc'): created from AIFdb with 'deepa2 bake' as described here.
* 'aaac' ('aaac01' and 'aaac02'): original, machine-generated contribution; based on an an improved and extended algorithm that backs URL ### Data Instances


see: URL ### Data Fields


see: URL ### Data Splits


Each sub-dataset contains three splits: 'train', 'validation', and 'test'.


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


Many NLP datasets focus on tasks that are relevant for logical analysis and argument reconstruction. This collection is the attempt to unify these resources in a common framework. ### Source Data


See: Sub-Datasets


Additional Information
---------------------- ### Dataset Curators


Gregor Betz, KIT; Kyle Richardson, Allen AI ### Licensing Information


We re-distribute the the imported sub-datasets under their original license:
passage: TAGS
#task_categories-text-retrieval #task_categories-text-generation #task_ids-text-simplification #task_ids-parsing #language_creators-other #multilinguality-monolingual #size_categories-unknown #language-English #license-other #argument-mining #summarization #conditional-text-generation #structure-prediction #arxiv-2110.01509 #region-us 
### Dataset Summary


This is a growing, curated collection of 'deepa2' datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises:


* datasets that are built from existing NLP datasets by means of the 'deepa2 bake' tool.
* original 'deepa2' datasets specifically created for this collection.


The tool 'deepa2 serve' may be used to render the data in this collection as text2text examples.### Supported Tasks and Leaderboards


For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the 'task-category-tag' with an appropriate 'other:other-task-name').


* 'conditional-text-generation': The dataset can be used to train models to generate a fully reconstruction of an argument from a source text, making, e.g., its implicit assumptions explicit.
* 'structure-prediction': The dataset can be used to train models to formalize sentences.
* 'text-retrieval': The dataset can be used to train models to extract reason statements and conjectures from a given source text.### Languages


English. Will be extended to cover other languages in the futures.


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