
# Dataset Card for "alsqa"

## Table of Contents
- [Dataset Card for "alsqa"](#dataset-card-for-alsqa)
  - [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)
    - [Data Instances](#data-instances)
      - [squad_v2](#squad_v2)
    - [Data Fields](#data-fields)
      - [squad_v2](#squad_v2-1)
    - [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:** [https://github.com/elronbandel/lexical-generalization](https://github.com/elronbandel/lexical-generalization)
- **Repository:** [https://github.com/elronbandel/lexical-generalization](https://github.com/elronbandel/lexical-generalization)
- **Paper:** [Lexical Generalization Improves with Larger Models and Longer Training](https://arxiv.org/abs/2210.12673)
- **Point of Contact:** [https://github.com/elronbandel/lexical-generalization](https://github.com/elronbandel/lexical-generalization)
- **Size of downloaded dataset files:** 100 KB
- **Size of the generated dataset:** 1 MB
- **Total amount of disk used:** 1 MB

### Dataset Summary

To test the lexical overlap heuristic utilization in Reading Comprehension models, we create a new test set: Analyzing Lexically Similar QA (ALSQA).
We augment the SQuAD 2.0 dataset (Rajpurkar et al., 2018) by asking crowdworkers to generate questions with high context-overlap from questions with low overlap (These questions are paraphrases of the original questions).
In the case of un-answerable questions, annotators were asked to re-write the question without changing its meaning and maintain the unanswerability reason.3 ALSQA contains 365 questions pairs, 190 with an- swer and 174 without answer.

## Dataset Structure

Identical to squad v2

#
### Data Fields

The data fields are the same among all splits.

#### alsqa
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.

### Data Splits

| name     |  test |
| -------- | -----: | 
| squad_v2 | 365 |  

## Dataset Creation

### Curation Rationale

### Source Data
squad_v2
#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Citation Information

```
@misc{https://doi.org/10.48550/arxiv.2210.12673,
  doi = {10.48550/ARXIV.2210.12673},
  url = {https://arxiv.org/abs/2210.12673},
  author = {Bandel, Elron and Goldberg, Yoav and Elazar, Yanai},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Lexical Generalization Improves with Larger Models and Longer Training},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

```


### Contributions

Thanks to [@elronbandel](https://github.com/elronbandel) for adding this dataset.
task_categories:question-answering task_categories:text-classification task_ids:open-domain-qa task_ids:extractive-qa annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:<1000 source_datasets:original language:en license:apache-2.0 arxiv:2210.12673 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["<1000"], "source_datasets": ["original"], "task_categories": ["question-answering", "text-classification"], "task_ids": ["open-domain-qa", "extractive-qa"], "paperswithcode_id": "alsqa", "pretty_name": "ALSQA", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "config_name": "alsqa"}}
TAGS
#task_categories-question-answering #task_categories-text-classification #task_ids-open-domain-qa #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-<1000 #source_datasets-original #language-English #license-apache-2.0 #arxiv-2210.12673 #region-us 

Dataset Card for "alsqa"
========================


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


* Dataset Card for "alsqa"
	+ Table of Contents
	+ Dataset Description
		- Dataset Summary
		- Supported Tasks and Leaderboards
		- Languages
	+ Dataset Structure
		- Data Instances
			* squad\_v2
		- Data Fields
			* squad\_v2
		- 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: URL
* Repository: URL
* Paper: Lexical Generalization Improves with Larger Models and Longer Training
* Point of Contact: URL
* Size of downloaded dataset files: 100 KB
* Size of the generated dataset: 1 MB
* Total amount of disk used: 1 MB


### Dataset Summary


To test the lexical overlap heuristic utilization in Reading Comprehension models, we create a new test set: Analyzing Lexically Similar QA (ALSQA).
We augment the SQuAD 2.0 dataset (Rajpurkar et al., 2018) by asking crowdworkers to generate questions with high context-overlap from questions with low overlap (These questions are paraphrases of the original questions).
In the case of un-answerable questions, annotators were asked to re-write the question without changing its meaning and maintain the unanswerability reason.3 ALSQA contains 365 questions pairs, 190 with an- swer and 174 without answer.


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


Identical to squad v2



### Data Fields


The data fields are the same among all splits.


#### alsqa


* 'id': a 'string' feature.
* 'title': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.


### Data Splits



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


### Curation Rationale


### Source Data


squad\_v2


#### 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


### Contributions


Thanks to @elronbandel for adding this dataset.



### Dataset Summary


To test the lexical overlap heuristic utilization in Reading Comprehension models, we create a new test set: Analyzing Lexically Similar QA (ALSQA).
We augment the SQuAD 2.0 dataset (Rajpurkar et al., 2018) by asking crowdworkers to generate questions with high context-overlap from questions with low overlap (These questions are paraphrases of the original questions).
In the case of un-answerable questions, annotators were asked to re-write the question without changing its meaning and maintain the unanswerability reason.3 ALSQA contains 365 questions pairs, 190 with an- swer and 174 without answer.


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


Identical to squad v2 ### Data Fields


The data fields are the same among all splits. #### alsqa


* 'id': a 'string' feature.
* 'title': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data


squad\_v2 #### 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 ### Contributions


Thanks to @elronbandel for adding this dataset.
TAGS
#task_categories-question-answering #task_categories-text-classification #task_ids-open-domain-qa #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-<1000 #source_datasets-original #language-English #license-apache-2.0 #arxiv-2210.12673 #region-us 
 ### Dataset Summary


To test the lexical overlap heuristic utilization in Reading Comprehension models, we create a new test set: Analyzing Lexically Similar QA (ALSQA).
We augment the SQuAD 2.0 dataset (Rajpurkar et al., 2018) by asking crowdworkers to generate questions with high context-overlap from questions with low overlap (These questions are paraphrases of the original questions).
In the case of un-answerable questions, annotators were asked to re-write the question without changing its meaning and maintain the unanswerability reason.3 ALSQA contains 365 questions pairs, 190 with an- swer and 174 without answer.


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


Identical to squad v2 ### Data Fields


The data fields are the same among all splits. #### alsqa


* 'id': a 'string' feature.
* 'title': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data


squad\_v2 #### 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 ### Contributions


Thanks to @elronbandel for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_categories-text-classification #task_ids-open-domain-qa #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-<1000 #source_datasets-original #language-English #license-apache-2.0 #arxiv-2210.12673 #region-us 
### Dataset Summary


To test the lexical overlap heuristic utilization in Reading Comprehension models, we create a new test set: Analyzing Lexically Similar QA (ALSQA).
We augment the SQuAD 2.0 dataset (Rajpurkar et al., 2018) by asking crowdworkers to generate questions with high context-overlap from questions with low overlap (These questions are paraphrases of the original questions).
In the case of un-answerable questions, annotators were asked to re-write the question without changing its meaning and maintain the unanswerability reason.3 ALSQA contains 365 questions pairs, 190 with an- swer and 174 without answer.


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


Identical to squad v2### Data Fields


The data fields are the same among all splits.#### alsqa


* 'id': a 'string' feature.
* 'title': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.### Data Splits



Dataset Creation
----------------### Curation Rationale### Source Data


squad\_v2#### 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