
# Dataset Card for "lmqg/qg_esquad"

## 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 subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
 ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
This is a modified version of [SQuAD-es](https://huggingface.co/datasets/squad_es) for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set.

### Supported Tasks and Leaderboards
* `question-generation`: The dataset is assumed to be used to train a model for question generation.
  Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).

### Languages
Spanish (es)

## Dataset Structure
An example of 'train' looks as follows.
```
{
  'answer': 'comedia musical',
  'question': '¿Qué género de película protagonizó Beyonce con Cuba Gooding, Jr?',                                                                             
  'sentence': 'en la comedia musical ',
  'paragraph': 'En julio de 2002, Beyoncé continuó su carrera como actriz interpretando a Foxxy Cleopatra junto a Mike Myers en la película de comedia, Austin Powers in Goldmember, que pasó su primer fin de semana en la cima de la taquilla de Estados Unidos. Beyoncé lanzó "Work It Out" como el primer sencillo de su álbum de banda sonora que entró en el top ten en el Reino Unido, Noruega y Bélgica. En 2003, Knowles protagonizó junto a Cuba Gooding, Jr., en la comedia musical The Fighting Temptations como Lilly, una madre soltera de quien el personaje de Gooding se enamora. Beyoncé lanzó "Fighting Temptation" como el primer sencillo de la banda sonora de la película, con Missy Elliott, MC Lyte y Free que también se utilizó para promocionar la película. Otra de las contribuciones de Beyoncé a la banda sonora, "Summertime", fue mejor en las listas de Estados Unidos.',
  'sentence_answer': 'en la <hl> comedia musical <hl> ',
  'paragraph_answer': 'En julio de 2002, Beyoncé continuó su carrera como actriz interpretando a Foxxy Cleopatra junto a Mike Myers en la película de comedia, Austin Powers in Goldmember, que pasó su primer fin de semana en la cima de la taquilla de Estados Unidos. Beyoncé lanzó "Work It Out" como el primer sencillo de su álbum de banda sonora que entró en el top ten en el Reino Unido, Noruega y Bélgica. En 2003, Knowles protagonizó junto a Cuba Gooding, Jr., en la <hl> comedia musical <hl> The Fighting Temptations como Lilly, una madre soltera de quien el personaje de Gooding se enamora. Beyoncé lanzó "Fighting Temptation" como el primer sencillo de la banda sonora de la película, con Missy Elliott, MC Lyte y Free que también se utilizó para promocionar la película. Otra de las contribuciones de Beyoncé a la banda sonora, "Summertime", fue mejor en las listas de Estados Unidos.',
  'paragraph_sentence': 'En julio de 2002, Beyoncé continuó su carrera como actriz interpretando a Foxxy Cleopatra junto a Mike Myers en la película de comedia, Austin Powers in Goldmember, que pasó su primer fin de semana en la cima de la taquilla de Estados Unidos. Beyoncé lanzó "Work It Out" como el primer sencillo de su álbum de banda sonora que entró en el top ten en el Reino Unido, Noruega y Bélgica. En 2003, Knowles protagonizó junto a Cuba Gooding, Jr. , <hl> en la comedia musical <hl> The Fighting Temptations como Lilly, una madre soltera de quien el personaje de Gooding se enamora. Beyoncé lanzó "Fighting Temptation" como el primer sencillo de la banda sonora de la película, con Missy Elliott, MC Lyte y Free que también se utilizó para promocionar la película. Otra de las contribuciones de Beyoncé a la banda sonora, "Summertime", fue mejor en las listas de Estados Unidos.',
}
```
The data fields are the same among all splits.
- `question`: a `string` feature. 
- `paragraph`: a `string` feature.
- `answer`: a `string` feature.
- `sentence`: a `string` feature.
- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.

Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and 
`paragraph_sentence` feature is for sentence-aware question generation.

## Data Splits

|train|validation|test |
|----:|---------:|----:|
|77025|    10570 |10570|


## 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:text-generation task_ids:language-modeling multilinguality:monolingual size_categories:10K<n<100K source_datasets:squad_es language:es license:cc-by-4.0 question-generation arxiv:2210.03992 region:us
{"language": "es", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": "10K<n<100K", "source_datasets": "squad_es", "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "SQuAD-es for question generation", "tags": ["question-generation"]}
TAGS
#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Spanish #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us 

Dataset Card for "lmqg/qg\_esquad"
==================================


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


* Repository: URL
* Paper: URL
* Point of Contact: Asahi Ushio


### Dataset Summary


This is a subset of QG-Bench, a unified question generation benchmark proposed in
"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference".
This is a modified version of SQuAD-es for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set.


### Supported Tasks and Leaderboards


* 'question-generation': The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).


### Languages


Spanish (es)


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


An example of 'train' looks as follows.


The data fields are the same among all splits.


* 'question': a 'string' feature.
* 'paragraph': a 'string' feature.
* 'answer': a 'string' feature.
* 'sentence': a 'string' feature.
* 'paragraph\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.
* 'paragraph\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.
* 'sentence\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.


Each of 'paragraph\_answer', 'paragraph\_sentence', and 'sentence\_answer' feature is assumed to be used to train a question generation model,
but with different information. The 'paragraph\_answer' and 'sentence\_answer' features are for answer-aware question generation and
'paragraph\_sentence' feature is for sentence-aware question generation.


Data Splits
-----------




### Dataset Summary


This is a subset of QG-Bench, a unified question generation benchmark proposed in
"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference".
This is a modified version of SQuAD-es for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set. ### Supported Tasks and Leaderboards


* 'question-generation': The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). ### Languages


Spanish (es)


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


An example of 'train' looks as follows.


The data fields are the same among all splits.


* 'question': a 'string' feature.
* 'paragraph': a 'string' feature.
* 'answer': a 'string' feature.
* 'sentence': a 'string' feature.
* 'paragraph\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.
* 'paragraph\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.
* 'sentence\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.


Each of 'paragraph\_answer', 'paragraph\_sentence', and 'sentence\_answer' feature is assumed to be used to train a question generation model,
but with different information. The 'paragraph\_answer' and 'sentence\_answer' features are for answer-aware question generation and
'paragraph\_sentence' feature is for sentence-aware question generation.


Data Splits
-----------
TAGS
#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Spanish #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us 
 ### Dataset Summary


This is a subset of QG-Bench, a unified question generation benchmark proposed in
"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference".
This is a modified version of SQuAD-es for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set. ### Supported Tasks and Leaderboards


* 'question-generation': The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). ### Languages


Spanish (es)


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


An example of 'train' looks as follows.


The data fields are the same among all splits.


* 'question': a 'string' feature.
* 'paragraph': a 'string' feature.
* 'answer': a 'string' feature.
* 'sentence': a 'string' feature.
* 'paragraph\_answer': a 'string' feature, which is same as the paragraph but the answer is highlighted by a special token ''.
* 'paragraph\_sentence': a 'string' feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token ''.
* 'sentence\_answer': a 'string' feature, which is same as the sentence but the answer is highlighted by a special token ''.


Each of 'paragraph\_answer', 'paragraph\_sentence', and 'sentence\_answer' feature is assumed to be used to train a question generation model,
but with different information. The 'paragraph\_answer' and 'sentence\_answer' features are for answer-aware question generation and
'paragraph\_sentence' feature is for sentence-aware question generation.


Data Splits
-----------
passage: TAGS
#task_categories-text-generation #task_ids-language-modeling #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-squad_es #language-Spanish #license-cc-by-4.0 #question-generation #arxiv-2210.03992 #region-us 
### Dataset Summary


This is a subset of QG-Bench, a unified question generation benchmark proposed in
"Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference".
This is a modified version of SQuAD-es for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set.### Supported Tasks and Leaderboards


* 'question-generation': The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).