Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
German
Size:
10K<n<100K
ArXiv:
Tags:
question-generation
License:
license: cc-by-4.0 | |
pretty_name: GermanQuAD for question generation | |
language: de | |
multilinguality: monolingual | |
size_categories: 10K<n<100K | |
source_datasets: deepset/germanquad | |
task_categories: question-generation | |
task_ids: question-generation | |
# Dataset Card for "lmqg/qg_dequad" | |
## 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 [GermanQuAD](https://huggingface.co/datasets/deepset/germanquad) 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': 'elektromagnetischer Linearführungen', | |
'question': 'Was kann den Verschleiß des seillosen Aufzuges minimieren?', | |
'sentence': 'Im Rahmen der Forschungen an dem seillosen Aufzug wird ebenfalls an der Entwicklung elektromagnetischer Linearführungen gearbeitet, um den Verschleiß der seillosen Aufzugsanlage bei hohem Fahrkomfort zu minimieren.', | |
'paragraph': "Aufzugsanlage\n\n=== Seilloser Aufzug ===\nAn der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei durch z..." | |
'sentence_answer': "Im Rahmen der Forschungen an dem seillosen Aufzug wird ebenfalls an der Entwicklung <hl> elektromagnetischer Linearführungen <hl> gearbeitet, um den Verschleiß der seillosen Aufzugsanlage bei...", | |
'paragraph_answer': "Aufzugsanlage === Seilloser Aufzug === An der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei durc...", | |
'paragraph_sentence': "Aufzugsanlage === Seilloser Aufzug === An der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei du..." | |
} | |
``` | |
## Data Fields | |
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 | | |
|----:|---------:|----:| | |
|9314 | 2204 | 2204| | |
## 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", | |
} | |
``` |