Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
Korean
Size:
10K - 100K
ArXiv:
Tags:
question-generation
License:
File size: 8,793 Bytes
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---
license: cc-by-4-0
pretty_name: KorQuAD for question generation
languages: ko
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: squad_es
task_categories: question-generation
task_ids: question-generation
---
# Dataset Card for "qg_korquad"
## 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)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [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
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
Modified version of [KorQuAD](https://huggingface.co./datasets/squad_kor_v1) 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 can be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L score.
### Languages
Korean (ko)
## Dataset Structure
### Data Instances
#### plain_text
An example of 'train' looks as follows.
```
{
"question": "ν¨μν΄μνμ΄ μ£Όλͺ©νλ νꡬλ?",
"paragraph": "λ³νμ λν μ΄ν΄μ λ¬μ¬λ μμ°κ³Όνμ μμ΄μ μΌλ°μ μΈ μ£Όμ μ΄λ©°, λ―Έμ λΆνμ λ³νλ₯Ό νꡬνλ κ°λ ₯ν λꡬλ‘μ λ°μ λμλ€. ν¨μλ λ³ννλ μμ λ¬μ¬ν¨μ μμ΄μ μ€μΆμ μΈ κ°λ
μΌλ‘μ¨ λ μ€λ₯΄κ² λλ€. μ€μμ μ€λ³μλ‘ κ΅¬μ±λ ν¨μμ μλ°ν νκ΅¬κ° μ€ν΄μνμ΄λΌλ λΆμΌλ‘ μλ €μ§κ² λμκ³ , 볡μμμ λν μ΄μ κ°μ νꡬλΆμΌλ 볡μν΄μνμ΄λΌκ³ νλ€. ν¨μν΄μνμ ν¨μμ 곡κ°(νΉν 무νμ°¨μ)μ νꡬμ μ£Όλͺ©νλ€. ν¨μν΄μνμ λ§μ μμ©λΆμΌ μ€ νλκ° μμμνμ΄λ€. λ§μ λ¬Έμ λ€μ΄ μμ°μ€λ½κ² μκ³Ό κ·Έ μμ λ³νμ¨μ κ΄κ³λ‘ κ·μ°©λκ³ , μ΄λ¬ν λ¬Έμ λ€μ΄ λ―ΈλΆλ°©μ μμΌλ‘ λ€λ£¨μ΄μ§λ€. μμ°μ λ§μ νμλ€μ΄ λμνκ³λ‘ κΈ°μ λ μ μλ€. νΌλ μ΄λ‘ μ μ΄λ¬ν μμΈ‘ λΆκ°λ₯ν νμμ νꡬνλ λ° μλΉν κΈ°μ¬λ₯Ό νλ€.",
"answer": "ν¨μμ 곡κ°(νΉν 무νμ°¨μ)μ νꡬ",
"sentence": "ν¨μν΄μνμ ν¨μμ 곡κ°(νΉν 무νμ°¨μ)μ νꡬ μ μ£Όλͺ©νλ€.",
"paragraph_sentence": 'λ³νμ λν μ΄ν΄μ λ¬μ¬λ μμ°κ³Όνμ μμ΄μ μΌλ°μ μΈ μ£Όμ μ΄λ©°, λ―Έμ λΆνμ λ³νλ₯Ό νꡬνλ κ°λ ₯ν λꡬλ‘μ λ°μ λμλ€. ν¨μλ λ³ννλ μμ λ¬μ¬ν¨μ μμ΄μ μ€μΆμ μΈ κ°λ
μΌλ‘μ¨ λ μ€λ₯΄κ² λλ€. μ€μμ μ€λ³μλ‘ κ΅¬μ±λ ν¨μμ μλ°ν νκ΅¬κ° μ€ν΄μνμ΄λΌλ λΆμΌλ‘ μλ €μ§κ² λμκ³ , 볡μμμ λν μ΄μ κ°μ νꡬ λΆμΌλ 볡μν΄μνμ΄λΌκ³ νλ€. <hl> ν¨μν΄μνμ ν¨μμ 곡κ°(νΉν 무νμ°¨μ)μ νꡬ μ μ£Όλͺ©νλ€. <hl> ν¨μν΄μνμ λ§μ μμ©λΆμΌ μ€ νλκ° μμμνμ΄λ€. λ§μ λ¬Έμ λ€μ΄ μμ°μ€λ½κ² μκ³Ό κ·Έ μμ λ³νμ¨μ κ΄κ³λ‘ κ·μ°©λκ³ , μ΄λ¬ν λ¬Έμ λ€μ΄ λ―ΈλΆλ°©μ μμΌλ‘ λ€λ£¨μ΄μ§λ€. μμ°μ λ§μ νμλ€μ΄ λμνκ³λ‘ κΈ°μ λ μ μλ€. νΌλ μ΄λ‘ μ μ΄λ¬ν μμΈ‘ λΆκ°λ₯ν νμμ νꡬνλ λ° μλΉν κΈ°μ¬λ₯Ό νλ€.',
"paragraph_answer": 'λ³νμ λν μ΄ν΄μ λ¬μ¬λ μμ°κ³Όνμ μμ΄μ μΌλ°μ μΈ μ£Όμ μ΄λ©°, λ―Έμ λΆνμ λ³νλ₯Ό νꡬνλ κ°λ ₯ν λꡬλ‘μ λ°μ λμλ€. ν¨μλ λ³ννλ μμ λ¬μ¬ν¨μ μμ΄μ μ€μΆμ μΈ κ°λ
μΌλ‘μ¨ λ μ€λ₯΄κ² λλ€. μ€μμ μ€λ³μλ‘ κ΅¬μ±λ ν¨μμ μλ°ν νκ΅¬κ° μ€ν΄μνμ΄λΌλ λΆμΌλ‘ μλ €μ§κ² λμκ³ , 볡μμμ λν μ΄μ κ°μ νꡬ λΆμΌλ 볡μν΄μνμ΄λΌκ³ νλ€. ν¨μν΄μνμ <hl> ν¨μμ 곡κ°(νΉν 무νμ°¨μ)μ νꡬ <hl>μ μ£Όλͺ©νλ€. ν¨μν΄μνμ λ§μ μμ©λΆμΌ μ€ νλκ° μμμνμ΄λ€. λ§μ λ¬Έμ λ€μ΄ μμ°μ€λ½κ² μκ³Ό κ·Έ μμ λ³νμ¨μ κ΄κ³λ‘ κ·μ°©λκ³ , μ΄λ¬ν λ¬Έμ λ€μ΄ λ―ΈλΆλ°©μ μμΌλ‘ λ€λ£¨μ΄μ§λ€. μμ°μ λ§μ νμλ€μ΄ λμνκ³λ‘ κΈ°μ λ μ μλ€. νΌλ μ΄λ‘ μ μ΄λ¬ν μμΈ‘ λΆκ°λ₯ν νμμ νꡬνλ λ° μλΉν κΈ°μ¬λ₯Ό νλ€.',
"sentence_answer": "ν¨μν΄μνμ <hl> ν¨μμ 곡κ°(νΉν 무νμ°¨μ)μ νꡬ <hl> μ μ£Όλͺ©νλ€."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `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
| name |train|validation|test |
|----------|----:|---------:|----:|
|plain_text|18500| 2000 |2000 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### 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
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |