
# Dataset Card for "lmqg/qa_squadshifts_synthetic"

## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)

### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), made for question-answering based evaluation (QAE) for question generation model proposed by [Zhang and Bansal, 2019](https://aclanthology.org/D19-1253/).
The test split is the original validation set of [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), where the model should be evaluate on.

### Supported Tasks and Leaderboards

* `question-answering`

### Languages
English (en)

## Dataset Structure

### Data Fields
The data fields are the same among all splits.

#### plain_text

- `id`: a `string` feature of id
- `title`: a `string` feature of title of the paragraph 
- `context`: a `string` feature of paragraph 
- `question`: a `string` feature of question
- `answers`: a `json` feature of answers

### Data Splits
TBA

## Citation Information

```
@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}
```
task_categories:question-answering task_ids:extractive-qa multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|wikipedia language:en license:cc-by-4.0 arxiv:2210.03992 region:us
{"language": "en", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": "10K<n<100K", "source_datasets": ["extended|wikipedia"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "Synthetic QA dataset on SQuADShifts."}
TAGS
#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us 


# Dataset Card for "lmqg/qa_squadshifts_synthetic"

## Dataset Description
- Repository: URL
- Paper: URL
- Point of Contact: Asahi Ushio

### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa_squadshifts', made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The test split is the original validation set of 'lmqg/qa_squadshifts', where the model should be evaluate on.

### Supported Tasks and Leaderboards

* 'question-answering'

### Languages
English (en)

## Dataset Structure

### Data Fields
The data fields are the same among all splits.

#### plain_text

- 'id': a 'string' feature of id
- 'title': a 'string' feature of title of the paragraph 
- 'context': a 'string' feature of paragraph 
- 'question': a 'string' feature of question
- 'answers': a 'json' feature of answers

### Data Splits
TBA




# Dataset Card for "lmqg/qa_squadshifts_synthetic" ## Dataset Description
- Repository: URL
- Paper: URL
- Point of Contact: Asahi Ushio ### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa_squadshifts', made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The test split is the original validation set of 'lmqg/qa_squadshifts', where the model should be evaluate on. ### Supported Tasks and Leaderboards

* 'question-answering' ### Languages
English (en) ## Dataset Structure ### Data Fields
The data fields are the same among all splits. #### plain_text

- 'id': a 'string' feature of id
- 'title': a 'string' feature of title of the paragraph 
- 'context': a 'string' feature of paragraph 
- 'question': a 'string' feature of question
- 'answers': a 'json' feature of answers ### Data Splits
TBA
TAGS
#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us 
 # Dataset Card for "lmqg/qa_squadshifts_synthetic" ## Dataset Description
- Repository: URL
- Paper: URL
- Point of Contact: Asahi Ushio ### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa_squadshifts', made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The test split is the original validation set of 'lmqg/qa_squadshifts', where the model should be evaluate on. ### Supported Tasks and Leaderboards

* 'question-answering' ### Languages
English (en) ## Dataset Structure ### Data Fields
The data fields are the same among all splits. #### plain_text

- 'id': a 'string' feature of id
- 'title': a 'string' feature of title of the paragraph 
- 'context': a 'string' feature of paragraph 
- 'question': a 'string' feature of question
- 'answers': a 'json' feature of answers ### Data Splits
TBA
passage: TAGS
#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us 
# Dataset Card for "lmqg/qa_squadshifts_synthetic"## Dataset Description
- Repository: URL
- Paper: URL
- Point of Contact: Asahi Ushio### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa_squadshifts', made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The test split is the original validation set of 'lmqg/qa_squadshifts', where the model should be evaluate on.### Supported Tasks and Leaderboards

* 'question-answering'### Languages
English (en)## Dataset Structure### Data Fields
The data fields are the same among all splits.#### plain_text

- 'id': a 'string' feature of id
- 'title': a 'string' feature of title of the paragraph 
- 'context': a 'string' feature of paragraph 
- 'question': a 'string' feature of question
- 'answers': a 'json' feature of answers### Data Splits
TBA